Merge pull request #62 from genenetwork/spacing

Spacing and coding style.

1 files changed, 3584 insertions, 2965 deletions

diff --git a/src/gemma.cpp b/src/gemma.cpp
index 1a9ca9b..c72475b 100644
--- a/src/gemma.cpp
+++ b/src/gemma.cpp
@@ -16,427 +16,670 @@
     along with this program. If not, see <http://www.gnu.org/licenses/>.
 */
 
-#include <iostream>
+#include <cmath>
+#include <cstring>
+#include <ctime>
 #include <fstream>
+#include <iostream>
 #include <string>
-#include <cstring>
 #include <sys/stat.h>
-#include <ctime>
-#include <cmath>
 
-#include "gsl/gsl_vector.h"
-#include "gsl/gsl_matrix.h"
-#include "gsl/gsl_linalg.h"
 #include "gsl/gsl_blas.h"
-#include "gsl/gsl_eigen.h"
 #include "gsl/gsl_cdf.h"
+#include "gsl/gsl_eigen.h"
+#include "gsl/gsl_linalg.h"
+#include "gsl/gsl_matrix.h"
+#include "gsl/gsl_vector.h"
 
-#include "lapack.h"
-#include "io.h"
-#include "gemma.h"
-#include "vc.h"
-#include "lm.h"
 #include "bslmm.h"
 #include "bslmmdap.h"
+#include "gemma.h"
+#include "io.h"
+#include "lapack.h"
 #include "ldr.h"
+#include "lm.h"
 #include "lmm.h"
+#include "mathfunc.h"
 #include "mvlmm.h"
 #include "prdt.h"
 #include "varcov.h"
-#include "mathfunc.h"
+#include "vc.h"
 
 using namespace std;
 
-GEMMA::GEMMA(void):
-version("0.97"), date("07/27/2017"), year("2017")
-{}
-
-void GEMMA::PrintHeader (void) {
-  cout<<endl;
-  cout<<"*********************************************************"<<endl;
-  cout<<"  Genome-wide Efficient Mixed Model Association (GEMMA)  "<<endl;
-  cout<<"  Version "<<version<<", "<<date<<"                              "<<
-    endl;
-  cout<<"  Visit http://www.xzlab.org/software.html For Updates   "<<endl;
-  cout<<"  (C) "<<year<<" Xiang Zhou                                   "<<endl;
-  cout<<"  GNU General Public License                             "<<endl;
-  cout<<"  For Help, Type ./gemma -h                              "<<endl;
-  cout<<"*********************************************************"<<endl;
-  cout<<endl;
+GEMMA::GEMMA(void) : version("0.97"), date("07/27/2017"), year("2017") {}
+
+void GEMMA::PrintHeader(void) {
+  cout << endl;
+  cout << "*********************************************************" << endl;
+  cout << "  Genome-wide Efficient Mixed Model Association (GEMMA)  " << endl;
+  cout << "  Version " << version << ", " << date
+       << "                              " << endl;
+  cout << "  Visit http://www.xzlab.org/software.html For Updates   " << endl;
+  cout << "  (C) " << year << " Xiang Zhou                                   "
+       << endl;
+  cout << "  GNU General Public License                             " << endl;
+  cout << "  For Help, Type ./gemma -h                              " << endl;
+  cout << "*********************************************************" << endl;
+  cout << endl;
 
   return;
 }
 
-void GEMMA::PrintLicense (void) {
-	cout<<endl;
-	cout<<"The Software Is Distributed Under GNU General Public "<<
-	  "License, But May Also Require The Following Notifications."<<endl;
-	cout<<endl;
-
-	cout<<"Including Lapack Routines In The Software May Require"<<
-	  " The Following Notification:"<<endl;
-	cout<<"Copyright (c) 1992-2010 The University of Tennessee and "<<
-	  "The University of Tennessee Research Foundation.  All rights "<<
-	  "reserved."<<endl;
-	cout<<"Copyright (c) 2000-2010 The University of California "<<
-	  "Berkeley. All rights reserved."<<endl;
-	cout<<"Copyright (c) 2006-2010 The University of Colorado Denver. "<<
-	  "All rights reserved."<<endl;
-	cout<<endl;
-
-	cout<<"$COPYRIGHT$"<<endl;
-	cout<<"Additional copyrights may follow"<<endl;
-	cout<<"$HEADER$"<<endl;
-	cout<<"Redistribution and use in source and binary forms, with or "<<
-	  "without modification, are permitted provided that the following "<<
-	  " conditions are met:"<<endl;
-	cout<<"- Redistributions of source code must retain the above "<<
-	  "copyright notice, this list of conditions and the following "<<
-	  "disclaimer."<<endl;
-	cout<<"- Redistributions in binary form must reproduce the above "<<
-	  "copyright notice, this list of conditions and the following "<<
-	  "disclaimer listed in this license in the documentation and/or "<<
-	  "other materials provided with the distribution."<<endl;
-	cout<<"- Neither the name of the copyright holders nor the names "<<
-	  "of its contributors may be used to endorse or promote products "<<
-	  "derived from this software without specific prior written "<<
-	  "permission."<<endl;
-	cout<<"The copyright holders provide no reassurances that the "<<
-	  "source code provided does not infringe any patent, copyright, "<<
-	  "or any other "<<
-	  "intellectual property rights of third parties. "<<
-	  "The copyright holders disclaim any liability to any recipient "<<
-	  "for claims brought against "<<
-	  "recipient by any third party for infringement of that parties "<<
-	  "intellectual property rights. "<<endl;
-	cout<<"THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND "<<
-	  "CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, "<<
-	  "INCLUDING, BUT NOT "<<
-	  "LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND "<<
-	  "FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT "<<
-	  "SHALL THE COPYRIGHT "<<
-	  "OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, "<<
-	  "INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES "<<
-	  "(INCLUDING, BUT NOT "<<
-	  "LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; "<<
-	  "LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) "<<
-	  "HOWEVER CAUSED AND ON ANY "<<
-	  "THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, "<<
-	  "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY "<<
-	  "OUT OF THE USE "<<
-	  "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF "<<
-	  "SUCH DAMAGE."<<endl;
-	cout<<endl;
-
-	return;
+void GEMMA::PrintLicense(void) {
+  cout << endl;
+  cout << "The Software Is Distributed Under GNU General Public "
+       << "License, But May Also Require The Following Notifications." << endl;
+  cout << endl;
+
+  cout << "Including Lapack Routines In The Software May Require"
+       << " The Following Notification:" << endl;
+  cout << "Copyright (c) 1992-2010 The University of Tennessee and "
+       << "The University of Tennessee Research Foundation.  All rights "
+       << "reserved." << endl;
+  cout << "Copyright (c) 2000-2010 The University of California "
+       << "Berkeley. All rights reserved." << endl;
+  cout << "Copyright (c) 2006-2010 The University of Colorado Denver. "
+       << "All rights reserved." << endl;
+  cout << endl;
+
+  cout << "$COPYRIGHT$" << endl;
+  cout << "Additional copyrights may follow" << endl;
+  cout << "$HEADER$" << endl;
+  cout << "Redistribution and use in source and binary forms, with or "
+       << "without modification, are permitted provided that the following "
+       << " conditions are met:" << endl;
+  cout << "- Redistributions of source code must retain the above "
+       << "copyright notice, this list of conditions and the following "
+       << "disclaimer." << endl;
+  cout << "- Redistributions in binary form must reproduce the above "
+       << "copyright notice, this list of conditions and the following "
+       << "disclaimer listed in this license in the documentation and/or "
+       << "other materials provided with the distribution." << endl;
+  cout << "- Neither the name of the copyright holders nor the names "
+       << "of its contributors may be used to endorse or promote products "
+       << "derived from this software without specific prior written "
+       << "permission." << endl;
+  cout << "The copyright holders provide no reassurances that the "
+       << "source code provided does not infringe any patent, copyright, "
+       << "or any other "
+       << "intellectual property rights of third parties. "
+       << "The copyright holders disclaim any liability to any recipient "
+       << "for claims brought against "
+       << "recipient by any third party for infringement of that parties "
+       << "intellectual property rights. " << endl;
+  cout << "THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND "
+       << "CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, "
+       << "INCLUDING, BUT NOT "
+       << "LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND "
+       << "FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT "
+       << "SHALL THE COPYRIGHT "
+       << "OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, "
+       << "INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES "
+       << "(INCLUDING, BUT NOT "
+       << "LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; "
+       << "LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) "
+       << "HOWEVER CAUSED AND ON ANY "
+       << "THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, "
+       << "OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY "
+       << "OUT OF THE USE "
+       << "OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF "
+       << "SUCH DAMAGE." << endl;
+  cout << endl;
+
+  return;
 }
 
 void GEMMA::PrintHelp(size_t option) {
-  if (option==0) {
-    cout<<endl;
-    cout<<" GEMMA version "<<version<<", released on "<<date<<endl;
-    cout<<" implemented by Xiang Zhou"<<endl;
-    cout<<endl;
-    cout<<" type ./gemma -h [num] for detailed helps"<<endl;
-    cout<<" options: " << endl;
-    cout<<" 1: quick guide"<<endl;
-    cout<<" 2: file I/O related"<<endl;
-    cout<<" 3: SNP QC"<<endl;
-    cout<<" 4: calculate relatedness matrix"<<endl;
-    cout<<" 5: perform eigen decomposition"<<endl;
-    cout<<" 6: perform variance component estimation"<<endl;
-    cout<<" 7: fit a linear model"<<endl;
-    cout<<" 8: fit a linear mixed model"<<endl;
-    cout<<" 9: fit a multivariate linear mixed model"<<endl;
-    cout<<" 10: fit a Bayesian sparse linear mixed model"<<endl;
-    cout<<" 11: obtain predicted values"<<endl;
-    cout<<" 12: calculate snp variance covariance"<<endl;
-    cout<<" 13: note"<<endl;
-    cout<<endl;
+  if (option == 0) {
+    cout << endl;
+    cout << " GEMMA version " << version << ", released on " << date << endl;
+    cout << " implemented by Xiang Zhou" << endl;
+    cout << endl;
+    cout << " type ./gemma -h [num] for detailed helps" << endl;
+    cout << " options: " << endl;
+    cout << " 1: quick guide" << endl;
+    cout << " 2: file I/O related" << endl;
+    cout << " 3: SNP QC" << endl;
+    cout << " 4: calculate relatedness matrix" << endl;
+    cout << " 5: perform eigen decomposition" << endl;
+    cout << " 6: perform variance component estimation" << endl;
+    cout << " 7: fit a linear model" << endl;
+    cout << " 8: fit a linear mixed model" << endl;
+    cout << " 9: fit a multivariate linear mixed model" << endl;
+    cout << " 10: fit a Bayesian sparse linear mixed model" << endl;
+    cout << " 11: obtain predicted values" << endl;
+    cout << " 12: calculate snp variance covariance" << endl;
+    cout << " 13: note" << endl;
+    cout << endl;
   }
 
-  if (option==1) {
-    cout<<" QUICK GUIDE" << endl;
-    cout<<" to generate a relatedness matrix: "<<endl;
-    cout<<"         ./gemma -bfile [prefix] -gk [num] -o [prefix]"<<endl;
-    cout<<"         ./gemma -g [filename] -p [filename] -gk [num] -o [prefix]"<<endl;
-    cout<<" to generate the S matrix: "<<endl;
-    cout<<"         ./gemma -bfile [prefix] -gs -o [prefix]"<<endl;
-    cout<<"         ./gemma -p [filename] -g [filename] -gs -o [prefix]"<<endl;
-    cout<<"         ./gemma -bfile [prefix] -cat [filename] -gs -o [prefix]"<<endl;
-    cout<<"         ./gemma -p [filename] -g [filename] -cat [filename] -gs -o [prefix]"<<endl;
-    cout<<"         ./gemma -bfile [prefix] -sample [num] -gs -o [prefix]"<<endl;
-    cout<<"         ./gemma -p [filename] -g [filename] -sample [num] -gs -o [prefix]"<<endl;
-    cout<<" to generate the q vector: "<<endl;
-    cout<<"         ./gemma -beta [filename] -gq -o [prefix]"<<endl;
-    cout<<"         ./gemma -beta [filename] -cat [filename] -gq -o [prefix]"<<endl;
-    cout<<" to generate the ldsc weigthts: "<<endl;
-    cout<<"         ./gemma -beta [filename] -gw -o [prefix]"<<endl;
-    cout<<"         ./gemma -beta [filename] -cat [filename] -gw -o [prefix]"<<endl;
-    cout<<" to perform eigen decomposition of the relatedness matrix: "<<endl;
-    cout<<"         ./gemma -bfile [prefix] -k [filename] -eigen -o [prefix]"<<endl;
-    cout<<"         ./gemma -g [filename] -p [filename] -k [filename] -eigen -o [prefix]"<<endl;
-    cout<<" to estimate variance components: "<<endl;
-    cout<<"         ./gemma -bfile [prefix] -k [filename] -vc [num] -o [prefix]"<<endl;
-    cout<<"         ./gemma -p [filename] -k [filename] -vc [num] -o [prefix]"<<endl;
-    cout<<"         ./gemma -bfile [prefix] -mk [filename] -vc [num] -o [prefix]"<<endl;
-    cout<<"         ./gemma -p [filename] -mk [filename] -vc [num] -o [prefix]"<<endl;
-    cout<<"         ./gemma -beta [filename] -cor [filename] -vc [num] -o [prefix]"<<endl;
-    cout<<"         ./gemma -beta [filename] -cor [filename] -cat [filename] -vc [num] -o [prefix]"<<endl;
-    cout<<"         options for the above two commands: -crt -windowbp [num]"<<endl;
-    cout<<"         ./gemma -mq [filename] -ms [filename] -mv [filename] -vc [num] -o [prefix]"<<endl;
-    cout<<"         or with summary statistics, replace bfile with mbfile, or g or mg; vc=1 for HE weights and vc=2 for LDSC weights"<<endl;
-    cout<<"         ./gemma -beta [filename] -bfile [filename] -cat [filename] -wsnp [filename] -wcat [filename] -vc [num] -o [prefix]"<<endl;
-    cout<<"         ./gemma -beta [filename] -bfile [filename] -cat [filename] -wsnp [filename] -wcat [filename] -ci [num] -o [prefix]"<<endl;
-    cout<<" to fit a linear mixed model: "<<endl;
-    cout<<"         ./gemma -bfile [prefix] -k [filename] -lmm [num] -o [prefix]"<<endl;
-    cout<<"         ./gemma -g [filename] -p [filename] -a [filename] -k [filename] -lmm [num] -o [prefix]"<<endl;
-    cout<<" to fit a linear mixed model to test g by e effects: "<<endl;
-    cout<<"         ./gemma -bfile [prefix] -gxe [filename] -k [filename] -lmm [num] -o [prefix]"<<endl;
-    cout<<"         ./gemma -g [filename] -p [filename] -a [filename] -gxe [filename] -k [filename] -lmm [num] -o [prefix]"<<endl;
-    cout<<" to fit a univariate linear mixed model with different residual weights for different individuals: "<<endl;
-    cout<<"         ./gemma -bfile [prefix] -weight [filename] -k [filename] -lmm [num] -o [prefix]"<<endl;
-    cout<<"         ./gemma -g [filename] -p [filename] -a [filename] -weight [filename] -k [filename] -lmm [num] -o [prefix]"<<endl;
-    cout<<" to fit a multivariate linear mixed model: "<<endl;
-    cout<<"         ./gemma -bfile [prefix] -k [filename] -lmm [num] -n [num1] [num2] -o [prefix]"<<endl;
-    cout<<"         ./gemma -g [filename] -p [filename] -a [filename] -k [filename] -lmm [num] -n [num1] [num2] -o [prefix]"<<endl;
-    cout<<" to fit a Bayesian sparse linear mixed model: "<<endl;
-    cout<<"         ./gemma -bfile [prefix] -bslmm [num] -o [prefix]"<<endl;
-    cout<<"         ./gemma -g [filename] -p [filename] -a [filename] -bslmm [num] -o [prefix]"<<endl;
-    cout<<" to obtain predicted values: "<<endl;
-    cout<<"         ./gemma -bfile [prefix] -epm [filename] -emu [filename] -ebv [filename] -k [filename] -predict [num] -o [prefix]"<<endl;
-    cout<<"         ./gemma -g [filename] -p [filename] -epm [filename] -emu [filename] -ebv [filename] -k [filename] -predict [num] -o [prefix]"<<endl;
-    cout<<" to calculate correlations between SNPs: "<<endl;
-    cout<<"         ./gemma -bfile [prefix] -calccor -o [prefix]"<<endl;
-    cout<<"         ./gemma -g [filename] -p [filename] -calccor -o [prefix]"<<endl;
-    cout<<endl;
+  if (option == 1) {
+    cout << " QUICK GUIDE" << endl;
+    cout << " to generate a relatedness matrix: " << endl;
+    cout << "         ./gemma -bfile [prefix] -gk [num] -o [prefix]" << endl;
+    cout << "         ./gemma -g [filename] -p [filename] -gk [num] -o [prefix]"
+         << endl;
+    cout << " to generate the S matrix: " << endl;
+    cout << "         ./gemma -bfile [prefix] -gs -o [prefix]" << endl;
+    cout << "         ./gemma -p [filename] -g [filename] -gs -o [prefix]"
+         << endl;
+    cout << "         ./gemma -bfile [prefix] -cat [filename] -gs -o [prefix]"
+         << endl;
+    cout << "         ./gemma -p [filename] -g [filename] -cat [filename] -gs "
+            "-o [prefix]"
+         << endl;
+    cout << "         ./gemma -bfile [prefix] -sample [num] -gs -o [prefix]"
+         << endl;
+    cout << "         ./gemma -p [filename] -g [filename] -sample [num] -gs -o "
+            "[prefix]"
+         << endl;
+    cout << " to generate the q vector: " << endl;
+    cout << "         ./gemma -beta [filename] -gq -o [prefix]" << endl;
+    cout << "         ./gemma -beta [filename] -cat [filename] -gq -o [prefix]"
+         << endl;
+    cout << " to generate the ldsc weigthts: " << endl;
+    cout << "         ./gemma -beta [filename] -gw -o [prefix]" << endl;
+    cout << "         ./gemma -beta [filename] -cat [filename] -gw -o [prefix]"
+         << endl;
+    cout << " to perform eigen decomposition of the relatedness matrix: "
+         << endl;
+    cout << "         ./gemma -bfile [prefix] -k [filename] -eigen -o [prefix]"
+         << endl;
+    cout << "         ./gemma -g [filename] -p [filename] -k [filename] -eigen "
+            "-o [prefix]"
+         << endl;
+    cout << " to estimate variance components: " << endl;
+    cout << "         ./gemma -bfile [prefix] -k [filename] -vc [num] -o "
+            "[prefix]"
+         << endl;
+    cout << "         ./gemma -p [filename] -k [filename] -vc [num] -o [prefix]"
+         << endl;
+    cout << "         ./gemma -bfile [prefix] -mk [filename] -vc [num] -o "
+            "[prefix]"
+         << endl;
+    cout
+        << "         ./gemma -p [filename] -mk [filename] -vc [num] -o [prefix]"
+        << endl;
+    cout << "         ./gemma -beta [filename] -cor [filename] -vc [num] -o "
+            "[prefix]"
+         << endl;
+    cout << "         ./gemma -beta [filename] -cor [filename] -cat [filename] "
+            "-vc [num] -o [prefix]"
+         << endl;
+    cout << "         options for the above two commands: -crt -windowbp [num]"
+         << endl;
+    cout << "         ./gemma -mq [filename] -ms [filename] -mv [filename] -vc "
+            "[num] -o [prefix]"
+         << endl;
+    cout << "         or with summary statistics, replace bfile with mbfile, "
+            "or g or mg; vc=1 for HE weights and vc=2 for LDSC weights"
+         << endl;
+    cout << "         ./gemma -beta [filename] -bfile [filename] -cat "
+            "[filename] -wsnp [filename] -wcat [filename] -vc [num] -o [prefix]"
+         << endl;
+    cout << "         ./gemma -beta [filename] -bfile [filename] -cat "
+            "[filename] -wsnp [filename] -wcat [filename] -ci [num] -o [prefix]"
+         << endl;
+    cout << " to fit a linear mixed model: " << endl;
+    cout << "         ./gemma -bfile [prefix] -k [filename] -lmm [num] -o "
+            "[prefix]"
+         << endl;
+    cout << "         ./gemma -g [filename] -p [filename] -a [filename] -k "
+            "[filename] -lmm [num] -o [prefix]"
+         << endl;
+    cout << " to fit a linear mixed model to test g by e effects: " << endl;
+    cout << "         ./gemma -bfile [prefix] -gxe [filename] -k [filename] "
+            "-lmm [num] -o [prefix]"
+         << endl;
+    cout << "         ./gemma -g [filename] -p [filename] -a [filename] -gxe "
+            "[filename] -k [filename] -lmm [num] -o [prefix]"
+         << endl;
+    cout << " to fit a univariate linear mixed model with different residual "
+            "weights for different individuals: "
+         << endl;
+    cout << "         ./gemma -bfile [prefix] -weight [filename] -k [filename] "
+            "-lmm [num] -o [prefix]"
+         << endl;
+    cout << "         ./gemma -g [filename] -p [filename] -a [filename] "
+            "-weight [filename] -k [filename] -lmm [num] -o [prefix]"
+         << endl;
+    cout << " to fit a multivariate linear mixed model: " << endl;
+    cout << "         ./gemma -bfile [prefix] -k [filename] -lmm [num] -n "
+            "[num1] [num2] -o [prefix]"
+         << endl;
+    cout << "         ./gemma -g [filename] -p [filename] -a [filename] -k "
+            "[filename] -lmm [num] -n [num1] [num2] -o [prefix]"
+         << endl;
+    cout << " to fit a Bayesian sparse linear mixed model: " << endl;
+    cout << "         ./gemma -bfile [prefix] -bslmm [num] -o [prefix]" << endl;
+    cout << "         ./gemma -g [filename] -p [filename] -a [filename] -bslmm "
+            "[num] -o [prefix]"
+         << endl;
+    cout << " to obtain predicted values: " << endl;
+    cout << "         ./gemma -bfile [prefix] -epm [filename] -emu [filename] "
+            "-ebv [filename] -k [filename] -predict [num] -o [prefix]"
+         << endl;
+    cout << "         ./gemma -g [filename] -p [filename] -epm [filename] -emu "
+            "[filename] -ebv [filename] -k [filename] -predict [num] -o "
+            "[prefix]"
+         << endl;
+    cout << " to calculate correlations between SNPs: " << endl;
+    cout << "         ./gemma -bfile [prefix] -calccor -o [prefix]" << endl;
+    cout << "         ./gemma -g [filename] -p [filename] -calccor -o [prefix]"
+         << endl;
+    cout << endl;
   }
 
-  if (option==2) {
-    cout<<" FILE I/O RELATED OPTIONS" << endl;
-    cout<<" -bfile    [prefix]       "<<" specify input PLINK binary ped file prefix."<<endl;
-    cout<<"          requires: *.fam, *.bim and *.bed files"<<endl;
-    cout<<"          missing value: -9"<<endl;
-    cout<<" -g        [filename]     "<<" specify input BIMBAM mean genotype file name"<<endl;
-    cout<<"          format: rs#1, allele0, allele1, genotype for individual 1, genotype for individual 2, ..."<<endl;
-    cout<<"                  rs#2, allele0, allele1, genotype for individual 1, genotype for individual 2, ..."<<endl;
-    cout<<"                  ..."<<endl;
-    cout<<"          missing value: NA"<<endl;
-    cout<<" -p        [filename]     "<<" specify input BIMBAM phenotype file name"<<endl;
-    cout<<"          format: phenotype for individual 1"<<endl;
-    cout<<"                  phenotype for individual 2"<<endl;
-    cout<<"                  ..."<<endl;
-    cout<<"          missing value: NA"<<endl;
-    cout<<" -a        [filename]     "<<" specify input BIMBAM SNP annotation file name (optional)"<<endl;
-    cout<<"          format: rs#1, base_position, chr_number"<<endl;
-    cout<<"                  rs#2, base_position, chr_number"<<endl;
-    cout<<"                  ..."<<endl;
+  if (option == 2) {
+    cout << " FILE I/O RELATED OPTIONS" << endl;
+    cout << " -bfile    [prefix]       "
+         << " specify input PLINK binary ped file prefix." << endl;
+    cout << "          requires: *.fam, *.bim and *.bed files" << endl;
+    cout << "          missing value: -9" << endl;
+    cout << " -g        [filename]     "
+         << " specify input BIMBAM mean genotype file name" << endl;
+    cout << "          format: rs#1, allele0, allele1, genotype for individual "
+            "1, genotype for individual 2, ..."
+         << endl;
+    cout << "                  rs#2, allele0, allele1, genotype for individual "
+            "1, genotype for individual 2, ..."
+         << endl;
+    cout << "                  ..." << endl;
+    cout << "          missing value: NA" << endl;
+    cout << " -p        [filename]     "
+         << " specify input BIMBAM phenotype file name" << endl;
+    cout << "          format: phenotype for individual 1" << endl;
+    cout << "                  phenotype for individual 2" << endl;
+    cout << "                  ..." << endl;
+    cout << "          missing value: NA" << endl;
+    cout << " -a        [filename]     "
+         << " specify input BIMBAM SNP annotation file name (optional)" << endl;
+    cout << "          format: rs#1, base_position, chr_number" << endl;
+    cout << "                  rs#2, base_position, chr_number" << endl;
+    cout << "                  ..." << endl;
 
     // WJA added.
-    cout<<" -oxford    [prefix]       "<<" specify input Oxford genotype bgen file prefix."<<endl;
-    cout<<"          requires: *.bgen, *.sample files"<<endl;
-
-    cout<<" -gxe      [filename]     "<<" specify input file that contains a column of environmental factor for g by e tests"<<endl;
-    cout<<"          format: variable for individual 1"<<endl;
-    cout<<"                  variable for individual 2"<<endl;
-    cout<<"                  ..."<<endl;
-    cout<<"          missing value: NA"<<endl;
-    cout<<" -widv   [filename]     "<<" specify input file that contains a column of residual weights"<<endl;
-    cout<<"          format: variable for individual 1"<<endl;
-    cout<<"                  variable for individual 2"<<endl;
-    cout<<"                  ..."<<endl;
-    cout<<"          missing value: NA"<<endl;
-    cout<<" -k        [filename]     "<<" specify input kinship/relatedness matrix file name"<<endl;
-    cout<<" -mk       [filename]     "<<" specify input file which contains a list of kinship/relatedness matrices"<<endl;
-    cout<<" -u        [filename]     "<<" specify input file containing the eigen vectors of the kinship/relatedness matrix"<<endl;
-    cout<<" -d        [filename]     "<<" specify input file containing the eigen values of the kinship/relatedness matrix"<<endl;
-    cout<<" -c        [filename]     "<<" specify input covariates file name (optional)"<<endl;
-    cout<<" -cat      [filename]     "<<" specify input category file name (optional), which contains rs cat1 cat2 ..."<<endl;
-    cout<<" -beta     [filename]     "<<" specify input beta file name (optional), which contains rs beta se_beta n_total (or n_mis and n_obs) estimates from a lm model"<<endl;
-    cout<<" -cor      [filename]     "<<" specify input correlation file name (optional), which contains rs window_size correlations from snps"<<endl;
-    cout<<"          missing value: NA"<<endl;
-    cout<<"          note: the intercept (a column of 1s) may need to be included"<<endl;
-    cout<<" -epm      [filename]     "<<" specify input estimated parameter file name"<<endl;
-    cout<<" -en [n1] [n2] [n3] [n4]  "<<" specify values for the input estimated parameter file (with a header)"<<endl;
-    cout<<"          options: n1: rs column number"<<endl;
-    cout<<"                   n2: estimated alpha column number (0 to ignore)"<<endl;
-    cout<<"                   n3: estimated beta column number (0 to ignore)"<<endl;
-    cout<<"                   n4: estimated gamma column number (0 to ignore)"<<endl;
-    cout<<"          default: 2 4 5 6 if -ebv is not specified; 2 0 5 6 if -ebv is specified"<<endl;
-    cout<<" -ebv      [filename]     "<<" specify input estimated random effect (breeding value) file name"<<endl;
-    cout<<"          format: value for individual 1"<<endl;
-    cout<<"                  value for individual 2"<<endl;
-    cout<<"                  ..."<<endl;
-    cout<<"          missing value: NA"<<endl;
-    cout<<" -emu      [filename]     "<<" specify input log file name containing estimated mean"<<endl;
-    cout<<" -mu       [num]          "<<" specify input estimated mean value"<<endl;
-    cout<<" -gene     [filename]     "<<" specify input gene expression file name"<<endl;
-    cout<<"          format: header"<<endl;
-    cout<<"                  gene1, count for individual 1, count for individual 2, ..."<<endl;
-    cout<<"                  gene2, count for individual 1, count for individual 2, ..."<<endl;
-    cout<<"                  ..."<<endl;
-    cout<<"          missing value: not allowed"<<endl;
-    cout<<" -r        [filename]     "<<" specify input total read count file name"<<endl;
-    cout<<"          format: total read count for individual 1"<<endl;
-    cout<<"                  total read count for individual 2"<<endl;
-    cout<<"                  ..."<<endl;
-    cout<<"          missing value: NA"<<endl;
-    cout<<" -snps     [filename]     "<<" specify input snps file name to only analyze a certain set of snps"<<endl;
-    cout<<"          format: rs#1"<<endl;
-    cout<<"                  rs#2"<<endl;
-    cout<<"                  ..."<<endl;
-    cout<<"          missing value: NA"<<endl;
-    cout<<" -silence                 "<<" silent terminal display"<<endl;
-    cout<<" -km       [num]          "<<" specify input kinship/relatedness file type (default 1)."<<endl;
-    cout<<"          options: 1: \"n by n matrix\" format"<<endl;
-    cout<<"                   2: \"id  id  value\" format"<<endl;
-    cout<<" -n        [num]          "<<" specify phenotype column in the phenotype/*.fam file (optional; default 1)"<<endl;
-    cout<<" -pace     [num]          "<<" specify terminal display update pace (default 100000 SNPs or 100000 iterations)."<<endl;
-    cout<<" -outdir   [path]         "<<" specify output directory path (default \"./output/\")"<<endl;
-    cout<<" -o        [prefix]       "<<" specify output file prefix (default \"result\")"<<endl;
-    cout<<"          output: prefix.cXX.txt or prefix.sXX.txt from kinship/relatedness matrix estimation"<<endl;
-    cout<<"          output: prefix.assoc.txt and prefix.log.txt form association tests"<<endl;
-    cout<<endl;
+    cout << " -oxford    [prefix]       "
+         << " specify input Oxford genotype bgen file prefix." << endl;
+    cout << "          requires: *.bgen, *.sample files" << endl;
+
+    cout << " -gxe      [filename]     "
+         << " specify input file that contains a column of environmental "
+            "factor for g by e tests"
+         << endl;
+    cout << "          format: variable for individual 1" << endl;
+    cout << "                  variable for individual 2" << endl;
+    cout << "                  ..." << endl;
+    cout << "          missing value: NA" << endl;
+    cout << " -widv   [filename]     "
+         << " specify input file that contains a column of residual weights"
+         << endl;
+    cout << "          format: variable for individual 1" << endl;
+    cout << "                  variable for individual 2" << endl;
+    cout << "                  ..." << endl;
+    cout << "          missing value: NA" << endl;
+    cout << " -k        [filename]     "
+         << " specify input kinship/relatedness matrix file name" << endl;
+    cout << " -mk       [filename]     "
+         << " specify input file which contains a list of kinship/relatedness "
+            "matrices"
+         << endl;
+    cout << " -u        [filename]     "
+         << " specify input file containing the eigen vectors of the "
+            "kinship/relatedness matrix"
+         << endl;
+    cout << " -d        [filename]     "
+         << " specify input file containing the eigen values of the "
+            "kinship/relatedness matrix"
+         << endl;
+    cout << " -c        [filename]     "
+         << " specify input covariates file name (optional)" << endl;
+    cout << " -cat      [filename]     "
+         << " specify input category file name (optional), which contains rs "
+            "cat1 cat2 ..."
+         << endl;
+    cout << " -beta     [filename]     "
+         << " specify input beta file name (optional), which contains rs beta "
+            "se_beta n_total (or n_mis and n_obs) estimates from a lm model"
+         << endl;
+    cout << " -cor      [filename]     "
+         << " specify input correlation file name (optional), which contains "
+            "rs window_size correlations from snps"
+         << endl;
+    cout << "          missing value: NA" << endl;
+    cout << "          note: the intercept (a column of 1s) may need to be "
+            "included"
+         << endl;
+    cout << " -epm      [filename]     "
+         << " specify input estimated parameter file name" << endl;
+    cout << " -en [n1] [n2] [n3] [n4]  "
+         << " specify values for the input estimated parameter file (with a "
+            "header)"
+         << endl;
+    cout << "          options: n1: rs column number" << endl;
+    cout << "                   n2: estimated alpha column number (0 to ignore)"
+         << endl;
+    cout << "                   n3: estimated beta column number (0 to ignore)"
+         << endl;
+    cout << "                   n4: estimated gamma column number (0 to ignore)"
+         << endl;
+    cout << "          default: 2 4 5 6 if -ebv is not specified; 2 0 5 6 if "
+            "-ebv is specified"
+         << endl;
+    cout << " -ebv      [filename]     "
+         << " specify input estimated random effect (breeding value) file name"
+         << endl;
+    cout << "          format: value for individual 1" << endl;
+    cout << "                  value for individual 2" << endl;
+    cout << "                  ..." << endl;
+    cout << "          missing value: NA" << endl;
+    cout << " -emu      [filename]     "
+         << " specify input log file name containing estimated mean" << endl;
+    cout << " -mu       [num]          "
+         << " specify input estimated mean value" << endl;
+    cout << " -gene     [filename]     "
+         << " specify input gene expression file name" << endl;
+    cout << "          format: header" << endl;
+    cout << "                  gene1, count for individual 1, count for "
+            "individual 2, ..."
+         << endl;
+    cout << "                  gene2, count for individual 1, count for "
+            "individual 2, ..."
+         << endl;
+    cout << "                  ..." << endl;
+    cout << "          missing value: not allowed" << endl;
+    cout << " -r        [filename]     "
+         << " specify input total read count file name" << endl;
+    cout << "          format: total read count for individual 1" << endl;
+    cout << "                  total read count for individual 2" << endl;
+    cout << "                  ..." << endl;
+    cout << "          missing value: NA" << endl;
+    cout
+        << " -snps     [filename]     "
+        << " specify input snps file name to only analyze a certain set of snps"
+        << endl;
+    cout << "          format: rs#1" << endl;
+    cout << "                  rs#2" << endl;
+    cout << "                  ..." << endl;
+    cout << "          missing value: NA" << endl;
+    cout << " -silence                 "
+         << " silent terminal display" << endl;
+    cout << " -km       [num]          "
+         << " specify input kinship/relatedness file type (default 1)." << endl;
+    cout << "          options: 1: \"n by n matrix\" format" << endl;
+    cout << "                   2: \"id  id  value\" format" << endl;
+    cout << " -n        [num]          "
+         << " specify phenotype column in the phenotype/*.fam file (optional; "
+            "default 1)"
+         << endl;
+    cout << " -pace     [num]          "
+         << " specify terminal display update pace (default 100000 SNPs or "
+            "100000 iterations)."
+         << endl;
+    cout << " -outdir   [path]         "
+         << " specify output directory path (default \"./output/\")" << endl;
+    cout << " -o        [prefix]       "
+         << " specify output file prefix (default \"result\")" << endl;
+    cout << "          output: prefix.cXX.txt or prefix.sXX.txt from "
+            "kinship/relatedness matrix estimation"
+         << endl;
+    cout << "          output: prefix.assoc.txt and prefix.log.txt form "
+            "association tests"
+         << endl;
+    cout << endl;
   }
 
-  if (option==3) {
-    cout<<" SNP QC OPTIONS" << endl;
-    cout<<" -miss     [num]          "<<" specify missingness threshold (default 0.05)" << endl;
-    cout<<" -maf      [num]          "<<" specify minor allele frequency threshold (default 0.01)" << endl;
-    cout<<" -hwe      [num]          "<<" specify HWE test p value threshold (default 0; no test)" << endl;
-    cout<<" -r2       [num]          "<<" specify r-squared threshold (default 0.9999)" << endl;
-    cout<<" -notsnp                  "<<" minor allele frequency cutoff is not used" << endl;
-    cout<<endl;
+  if (option == 3) {
+    cout << " SNP QC OPTIONS" << endl;
+    cout << " -miss     [num]          "
+         << " specify missingness threshold (default 0.05)" << endl;
+    cout << " -maf      [num]          "
+         << " specify minor allele frequency threshold (default 0.01)" << endl;
+    cout << " -hwe      [num]          "
+         << " specify HWE test p value threshold (default 0; no test)" << endl;
+    cout << " -r2       [num]          "
+         << " specify r-squared threshold (default 0.9999)" << endl;
+    cout << " -notsnp                  "
+         << " minor allele frequency cutoff is not used" << endl;
+    cout << endl;
   }
 
-  if (option==4) {
-    cout<<" RELATEDNESS MATRIX CALCULATION OPTIONS" << endl;
-    cout<<" -gk       [num]          "<<" specify which type of kinship/relatedness matrix to generate (default 1)" << endl;
-    cout<<"          options: 1: centered XX^T/p"<<endl;
-    cout<<"                   2: standardized XX^T/p"<<endl;
-    cout<<"          note: non-polymorphic SNPs are excluded "<<endl;
-    cout<<endl;
+  if (option == 4) {
+    cout << " RELATEDNESS MATRIX CALCULATION OPTIONS" << endl;
+    cout << " -gk       [num]          "
+         << " specify which type of kinship/relatedness matrix to generate "
+            "(default 1)"
+         << endl;
+    cout << "          options: 1: centered XX^T/p" << endl;
+    cout << "                   2: standardized XX^T/p" << endl;
+    cout << "          note: non-polymorphic SNPs are excluded " << endl;
+    cout << endl;
   }
 
-  if (option==5) {
-    cout<<" EIGEN-DECOMPOSITION OPTIONS" << endl;
-    cout<<" -eigen                   "<<" specify to perform eigen decomposition of the loaded relatedness matrix" << endl;
-    cout<<endl;
+  if (option == 5) {
+    cout << " EIGEN-DECOMPOSITION OPTIONS" << endl;
+    cout << " -eigen                   "
+         << " specify to perform eigen decomposition of the loaded relatedness "
+            "matrix"
+         << endl;
+    cout << endl;
   }
 
-  if (option==6) {
-    cout<<" VARIANCE COMPONENT ESTIMATION OPTIONS" << endl;
-    cout<<" -vc                      "<<" specify to perform variance component estimation for the loaded relatedness matrix/matrices" << endl;
-    cout<<"          options (with kinship file):   1: HE regression (default)"<<endl;
-    cout<<"                                         2: REML"<<endl;
-    cout<<"          options (with beta/cor files): 1: Centered genotypes (default)"<<endl;
-    cout<<"                                         2: Standardized genotypes"<<endl;
-    cout<<"                                         -crt -windowbp [num]"<<" specify the window size based on bp (default 1000000; 1Mb)"<<endl;
-    cout<<"                                         -crt -windowcm [num]"<<" specify the window size based on cm (default 0)"<<endl;
-    cout<<"                                         -crt -windowns [num]"<<" specify the window size based on number of snps (default 0)"<<endl;
-    cout<<endl;
+  if (option == 6) {
+    cout << " VARIANCE COMPONENT ESTIMATION OPTIONS" << endl;
+    cout << " -vc                      "
+         << " specify to perform variance component estimation for the loaded "
+            "relatedness matrix/matrices"
+         << endl;
+    cout
+        << "          options (with kinship file):   1: HE regression (default)"
+        << endl;
+    cout << "                                         2: REML" << endl;
+    cout << "          options (with beta/cor files): 1: Centered genotypes "
+            "(default)"
+         << endl;
+    cout << "                                         2: Standardized genotypes"
+         << endl;
+    cout << "                                         -crt -windowbp [num]"
+         << " specify the window size based on bp (default 1000000; 1Mb)"
+         << endl;
+    cout << "                                         -crt -windowcm [num]"
+         << " specify the window size based on cm (default 0)" << endl;
+    cout << "                                         -crt -windowns [num]"
+         << " specify the window size based on number of snps (default 0)"
+         << endl;
+    cout << endl;
   }
 
-  if (option==7) {
-    cout<<" LINEAR MODEL OPTIONS" << endl;
-    cout<<" -lm       [num]         "<<" specify analysis options (default 1)."<<endl;
-    cout<<"          options: 1: Wald test"<<endl;
-    cout<<"                   2: Likelihood ratio test"<<endl;
-    cout<<"                   3: Score test"<<endl;
-    cout<<"                   4: 1-3"<<endl;
-    cout<<endl;
+  if (option == 7) {
+    cout << " LINEAR MODEL OPTIONS" << endl;
+    cout << " -lm       [num]         "
+         << " specify analysis options (default 1)." << endl;
+    cout << "          options: 1: Wald test" << endl;
+    cout << "                   2: Likelihood ratio test" << endl;
+    cout << "                   3: Score test" << endl;
+    cout << "                   4: 1-3" << endl;
+    cout << endl;
   }
 
-  if (option==8) {
-    cout<<" LINEAR MIXED MODEL OPTIONS" << endl;
-    cout<<" -lmm      [num]         "<<" specify analysis options (default 1)."<<endl;
-    cout<<"          options: 1: Wald test"<<endl;
-    cout<<"                   2: Likelihood ratio test"<<endl;
-    cout<<"                   3: Score test"<<endl;
-    cout<<"                   4: 1-3"<<endl;
-    cout<<"                   5: Parameter estimation in the null model only"<<endl;
-    cout<<" -lmin     [num]          "<<" specify minimal value for lambda (default 1e-5)" << endl;
-    cout<<" -lmax     [num]          "<<" specify maximum value for lambda (default 1e+5)" << endl;
-    cout<<" -region   [num]          "<<" specify the number of regions used to evaluate lambda (default 10)" << endl;
-    cout<<endl;
+  if (option == 8) {
+    cout << " LINEAR MIXED MODEL OPTIONS" << endl;
+    cout << " -lmm      [num]         "
+         << " specify analysis options (default 1)." << endl;
+    cout << "          options: 1: Wald test" << endl;
+    cout << "                   2: Likelihood ratio test" << endl;
+    cout << "                   3: Score test" << endl;
+    cout << "                   4: 1-3" << endl;
+    cout << "                   5: Parameter estimation in the null model only"
+         << endl;
+    cout << " -lmin     [num]          "
+         << " specify minimal value for lambda (default 1e-5)" << endl;
+    cout << " -lmax     [num]          "
+         << " specify maximum value for lambda (default 1e+5)" << endl;
+    cout
+        << " -region   [num]          "
+        << " specify the number of regions used to evaluate lambda (default 10)"
+        << endl;
+    cout << endl;
   }
 
-  if (option==9) {
-    cout<<" MULTIVARIATE LINEAR MIXED MODEL OPTIONS" << endl;
-    cout<<" -pnr				     "<<" specify the pvalue threshold to use the Newton-Raphson's method (default 0.001)"<<endl;
-    cout<<" -emi				     "<<" specify the maximum number of iterations for the PX-EM method in the null (default 10000)"<<endl;
-    cout<<" -nri				     "<<" specify the maximum number of iterations for the Newton-Raphson's method in the null (default 100)"<<endl;
-    cout<<" -emp				     "<<" specify the precision for the PX-EM method in the null (default 0.0001)"<<endl;
-    cout<<" -nrp				     "<<" specify the precision for the Newton-Raphson's method in the null (default 0.0001)"<<endl;
-    cout<<" -crt				     "<<" specify to output corrected pvalues for these pvalues that are below the -pnr threshold"<<endl;
-    cout<<endl;
+  if (option == 9) {
+    cout << " MULTIVARIATE LINEAR MIXED MODEL OPTIONS" << endl;
+    cout << " -pnr				     "
+         << " specify the pvalue threshold to use the Newton-Raphson's method "
+            "(default 0.001)"
+         << endl;
+    cout << " -emi				     "
+         << " specify the maximum number of iterations for the PX-EM method in "
+            "the null (default 10000)"
+         << endl;
+    cout << " -nri				     "
+         << " specify the maximum number of iterations for the "
+            "Newton-Raphson's method in the null (default 100)"
+         << endl;
+    cout << " -emp				     "
+         << " specify the precision for the PX-EM method in the null (default "
+            "0.0001)"
+         << endl;
+    cout << " -nrp				     "
+         << " specify the precision for the Newton-Raphson's method in the "
+            "null (default 0.0001)"
+         << endl;
+    cout << " -crt				     "
+         << " specify to output corrected pvalues for these pvalues that are "
+            "below the -pnr threshold"
+         << endl;
+    cout << endl;
   }
 
-  if (option==10) {
-    cout<<" MULTI-LOCUS ANALYSIS OPTIONS" << endl;
-    cout<<" -bslmm	  [num]			 "<<" specify analysis options (default 1)."<<endl;
-    cout<<"          options: 1: BSLMM"<<endl;
-    cout<<"                   2: standard ridge regression/GBLUP (no mcmc)"<<endl;
-    cout<<"                   3: probit BSLMM (requires 0/1 phenotypes)"<<endl;
-    cout<<"                   4: BSLMM with DAP for Hyper Parameter Estimation"<<endl;
-    cout<<"                   5: BSLMM with DAP for Fine Mapping"<<endl;
-
-    cout<<" -ldr	  [num]			 "<<" specify analysis options (default 1)."<<endl;
-    cout<<"          options: 1: LDR"<<endl;
-
-    cout<<"   MCMC OPTIONS" << endl;
-    cout<<"   Prior" << endl;
-    cout<<" -hmin     [num]          "<<" specify minimum value for h (default 0)" << endl;
-    cout<<" -hmax     [num]          "<<" specify maximum value for h (default 1)" << endl;
-    cout<<" -rmin     [num]          "<<" specify minimum value for rho (default 0)" << endl;
-    cout<<" -rmax     [num]          "<<" specify maximum value for rho (default 1)" << endl;
-    cout<<" -pmin     [num]          "<<" specify minimum value for log10(pi) (default log10(1/p), where p is the number of analyzed SNPs )" << endl;
-    cout<<" -pmax     [num]          "<<" specify maximum value for log10(pi) (default log10(1) )" << endl;
-    cout<<" -smin     [num]          "<<" specify minimum value for |gamma| (default 0)" << endl;
-    cout<<" -smax     [num]          "<<" specify maximum value for |gamma| (default 300)" << endl;
-
-    cout<<"   Proposal" << endl;
-    cout<<" -gmean    [num]          "<<" specify the mean for the geometric distribution (default: 2000)" << endl;
-    cout<<" -hscale   [num]          "<<" specify the step size scale for the proposal distribution of h (value between 0 and 1, default min(10/sqrt(n),1) )" << endl;
-    cout<<" -rscale   [num]          "<<" specify the step size scale for the proposal distribution of rho (value between 0 and 1, default min(10/sqrt(n),1) )" << endl;
-    cout<<" -pscale   [num]          "<<" specify the step size scale for the proposal distribution of log10(pi) (value between 0 and 1, default min(5/sqrt(n),1) )" << endl;
-
-    cout<<"   Others" << endl;
-    cout<<" -w        [num]          "<<" specify burn-in steps (default 100,000)" << endl;
-    cout<<" -s        [num]          "<<" specify sampling steps (default 1,000,000)" << endl;
-    cout<<" -rpace    [num]          "<<" specify recording pace, record one state in every [num] steps (default 10)" << endl;
-    cout<<" -wpace    [num]          "<<" specify writing pace, write values down in every [num] recorded steps (default 1000)" << endl;
-    cout<<" -seed     [num]          "<<" specify random seed (a random seed is generated by default)" << endl;
-    cout<<" -mh       [num]          "<<" specify number of MH steps in each iteration (default 10)" << endl;
-    cout<<"          requires: 0/1 phenotypes and -bslmm 3 option"<<endl;
-    cout<<endl;
+  if (option == 10) {
+    cout << " MULTI-LOCUS ANALYSIS OPTIONS" << endl;
+    cout << " -bslmm	  [num]			 "
+         << " specify analysis options (default 1)." << endl;
+    cout << "          options: 1: BSLMM" << endl;
+    cout << "                   2: standard ridge regression/GBLUP (no mcmc)"
+         << endl;
+    cout << "                   3: probit BSLMM (requires 0/1 phenotypes)"
+         << endl;
+    cout
+        << "                   4: BSLMM with DAP for Hyper Parameter Estimation"
+        << endl;
+    cout << "                   5: BSLMM with DAP for Fine Mapping" << endl;
+
+    cout << " -ldr	  [num]			 "
+         << " specify analysis options (default 1)." << endl;
+    cout << "          options: 1: LDR" << endl;
+
+    cout << "   MCMC OPTIONS" << endl;
+    cout << "   Prior" << endl;
+    cout << " -hmin     [num]          "
+         << " specify minimum value for h (default 0)" << endl;
+    cout << " -hmax     [num]          "
+         << " specify maximum value for h (default 1)" << endl;
+    cout << " -rmin     [num]          "
+         << " specify minimum value for rho (default 0)" << endl;
+    cout << " -rmax     [num]          "
+         << " specify maximum value for rho (default 1)" << endl;
+    cout << " -pmin     [num]          "
+         << " specify minimum value for log10(pi) (default log10(1/p), where p "
+            "is the number of analyzed SNPs )"
+         << endl;
+    cout << " -pmax     [num]          "
+         << " specify maximum value for log10(pi) (default log10(1) )" << endl;
+    cout << " -smin     [num]          "
+         << " specify minimum value for |gamma| (default 0)" << endl;
+    cout << " -smax     [num]          "
+         << " specify maximum value for |gamma| (default 300)" << endl;
+
+    cout << "   Proposal" << endl;
+    cout << " -gmean    [num]          "
+         << " specify the mean for the geometric distribution (default: 2000)"
+         << endl;
+    cout << " -hscale   [num]          "
+         << " specify the step size scale for the proposal distribution of h "
+            "(value between 0 and 1, default min(10/sqrt(n),1) )"
+         << endl;
+    cout << " -rscale   [num]          "
+         << " specify the step size scale for the proposal distribution of rho "
+            "(value between 0 and 1, default min(10/sqrt(n),1) )"
+         << endl;
+    cout << " -pscale   [num]          "
+         << " specify the step size scale for the proposal distribution of "
+            "log10(pi) (value between 0 and 1, default min(5/sqrt(n),1) )"
+         << endl;
+
+    cout << "   Others" << endl;
+    cout << " -w        [num]          "
+         << " specify burn-in steps (default 100,000)" << endl;
+    cout << " -s        [num]          "
+         << " specify sampling steps (default 1,000,000)" << endl;
+    cout << " -rpace    [num]          "
+         << " specify recording pace, record one state in every [num] steps "
+            "(default 10)"
+         << endl;
+    cout << " -wpace    [num]          "
+         << " specify writing pace, write values down in every [num] recorded "
+            "steps (default 1000)"
+         << endl;
+    cout << " -seed     [num]          "
+         << " specify random seed (a random seed is generated by default)"
+         << endl;
+    cout << " -mh       [num]          "
+         << " specify number of MH steps in each iteration (default 10)"
+         << endl;
+    cout << "          requires: 0/1 phenotypes and -bslmm 3 option" << endl;
+    cout << endl;
   }
 
-  if (option==11) {
-    cout<<" PREDICTION OPTIONS" << endl;
-    cout<<" -predict  [num]			 "<<" specify prediction options (default 1)."<<endl;
-    cout<<"          options: 1: predict for individuals with missing phenotypes"<<endl;
-    cout<<"                   2: predict for individuals with missing phenotypes, and convert the predicted values to probability scale. Use only for files fitted with -bslmm 3 option"<<endl;
-    cout<<endl;
+  if (option == 11) {
+    cout << " PREDICTION OPTIONS" << endl;
+    cout << " -predict  [num]			 "
+         << " specify prediction options (default 1)." << endl;
+    cout << "          options: 1: predict for individuals with missing "
+            "phenotypes"
+         << endl;
+    cout << "                   2: predict for individuals with missing "
+            "phenotypes, and convert the predicted values to probability "
+            "scale. Use only for files fitted with -bslmm 3 option"
+         << endl;
+    cout << endl;
   }
 
-  if (option==12) {
-    cout<<" CALC CORRELATION OPTIONS" << endl;
-    cout<<" -calccor       			 "<<endl;
-    cout<<" -windowbp       [num]            "<<" specify the window size based on bp (default 1000000; 1Mb)" << endl;
-    cout<<" -windowcm       [num]            "<<" specify the window size based on cm (default 0; not used)" << endl;
-    cout<<" -windowns       [num]            "<<" specify the window size based on number of snps (default 0; not used)" << endl;
-    cout<<endl;
+  if (option == 12) {
+    cout << " CALC CORRELATION OPTIONS" << endl;
+    cout << " -calccor       			 " << endl;
+    cout << " -windowbp       [num]            "
+         << " specify the window size based on bp (default 1000000; 1Mb)"
+         << endl;
+    cout << " -windowcm       [num]            "
+         << " specify the window size based on cm (default 0; not used)"
+         << endl;
+    cout << " -windowns       [num]            "
+         << " specify the window size based on number of snps (default 0; not "
+            "used)"
+         << endl;
+    cout << endl;
   }
 
-  if (option==13) {
-    cout<<" NOTE"<<endl;
-    cout<<" 1. Only individuals with non-missing phenotoypes and covariates will be analyzed."<<endl;
-    cout<<" 2. Missing genotoypes will be repalced with the mean genotype of that SNP."<<endl;
-    cout<<" 3. For lmm analysis, memory should be large enough to hold the relatedness matrix and to perform eigen decomposition."<<endl;
-    cout<<" 4. For multivariate lmm analysis, use a large -pnr for each snp will increase computation time dramatically."<<endl;
-    cout<<" 5. For bslmm analysis, in addition to 3, memory should be large enough to hold the whole genotype matrix."<<endl;
-    cout<<endl;
+  if (option == 13) {
+    cout << " NOTE" << endl;
+    cout << " 1. Only individuals with non-missing phenotoypes and covariates "
+            "will be analyzed."
+         << endl;
+    cout << " 2. Missing genotoypes will be repalced with the mean genotype of "
+            "that SNP."
+         << endl;
+    cout << " 3. For lmm analysis, memory should be large enough to hold the "
+            "relatedness matrix and to perform eigen decomposition."
+         << endl;
+    cout << " 4. For multivariate lmm analysis, use a large -pnr for each snp "
+            "will increase computation time dramatically."
+         << endl;
+    cout << " 5. For bslmm analysis, in addition to 3, memory should be large "
+            "enough to hold the whole genotype matrix."
+         << endl;
+    cout << endl;
   }
 
   return;
@@ -457,2609 +700,2985 @@ void GEMMA::PrintHelp(size_t option) {
 // calccor: 71
 // gw:      72
 
-void GEMMA::Assign(int argc, char ** argv, PARAM &cPar) {
-	string str;
-
-	for(int i = 1; i < argc; i++) {
-		if (strcmp(argv[i], "-bfile")==0 ||
-		    strcmp(argv[i], "--bfile")==0 ||
-		    strcmp(argv[i], "-b")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_bfile=str;
-		}
-		else if (strcmp(argv[i], "-mbfile")==0 ||
-			 strcmp(argv[i], "--mbfile")==0 ||
-			 strcmp(argv[i], "-mb")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_mbfile=str;
-		}
-		else if (strcmp(argv[i], "-silence")==0) {
-			cPar.mode_silence=true;
-		}
-		else if (strcmp(argv[i], "-g")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_geno=str;
-		}
-		else if (strcmp(argv[i], "-mg")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_mgeno=str;
-		}
-		else if (strcmp(argv[i], "-p")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_pheno=str;
-		}
-		else if (strcmp(argv[i], "-a")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_anno=str;
-		}
-
-		// WJA added.
-		else if (strcmp(argv[i], "-oxford")==0 ||
-			 strcmp(argv[i], "--oxford")==0 ||
-			 strcmp(argv[i], "-x")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_oxford=str;
-		}
-		else if (strcmp(argv[i], "-gxe")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_gxe=str;
-		}
-		else if (strcmp(argv[i], "-widv")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_weight=str;
-		}
-		else if (strcmp(argv[i], "-wsnp")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_wsnp=str;
-		}
-		else if (strcmp(argv[i], "-wcat")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_wcat=str;
-		}
-		else if (strcmp(argv[i], "-k")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_kin=str;
-		}
-		else if (strcmp(argv[i], "-mk")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_mk=str;
-		}
-		else if (strcmp(argv[i], "-u")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_ku=str;
-		}
-		else if (strcmp(argv[i], "-d")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_kd=str;
-		}
-		else if (strcmp(argv[i], "-c")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_cvt=str;
-		}
-		else if (strcmp(argv[i], "-cat")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_cat=str;
-		}
-		else if (strcmp(argv[i], "-mcat")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {
-			  continue;
-			}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_mcat=str;
-		}
-		else if (strcmp(argv[i], "-catc")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_catc=str;
-		}
-		else if (strcmp(argv[i], "-mcatc")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_mcatc=str;
-		}
-		else if (strcmp(argv[i], "-beta")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_beta=str;
-		}
-		else if (strcmp(argv[i], "-bf")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_bf=str;
-		}
-		else if (strcmp(argv[i], "-hyp")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_hyp=str;
-		}
-		else if (strcmp(argv[i], "-cor")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_cor=str;
-		}
-		else if (strcmp(argv[i], "-study")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_study=str;
-		}
-		else if (strcmp(argv[i], "-ref")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_ref=str;
-		}
-		else if (strcmp(argv[i], "-mstudy")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_mstudy=str;
-		}
-		else if (strcmp(argv[i], "-mref")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_mref=str;
-		}
-		else if (strcmp(argv[i], "-epm")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_epm=str;
-		}
-		else if (strcmp(argv[i], "-en")==0) {
-			while (argv[i+1] != NULL && argv[i+1][0] != '-') {
-				++i;
-				str.clear();
-				str.assign(argv[i]);
-				cPar.est_column.push_back(atoi(str.c_str()));
-			}
-		}
-		else if (strcmp(argv[i], "-ebv")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_ebv=str;
-		}
-		else if (strcmp(argv[i], "-emu")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_log=str;
-		}
-		else if (strcmp(argv[i], "-mu")==0) {
-			if(argv[i+1] == NULL) {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.pheno_mean=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-gene")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_gene=str;
-		}
-		else if (strcmp(argv[i], "-r")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_read=str;
-		}
-		else if (strcmp(argv[i], "-snps")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_snps=str;
-		}
-		else if (strcmp(argv[i], "-km")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.k_mode=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-n")==0) {
-			(cPar.p_column).clear();
-			while (argv[i+1] != NULL && argv[i+1][0] != '-') {
-				++i;
-				str.clear();
-				str.assign(argv[i]);
-				(cPar.p_column).push_back(atoi(str.c_str()));
-			}
-		}
-		else if (strcmp(argv[i], "-pace")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.d_pace=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-outdir")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.path_out=str;
-		}
-		else if (strcmp(argv[i], "-o")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.file_out=str;
-		}
-		else if (strcmp(argv[i], "-miss")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.miss_level=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-maf")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			if (cPar.maf_level!=-1) {cPar.maf_level=atof(str.c_str());}
-		}
-		else if (strcmp(argv[i], "-hwe")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.hwe_level=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-r2")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.r2_level=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-notsnp")==0) {
-			cPar.maf_level=-1;
-		}
-		else if (strcmp(argv[i], "-gk")==0) {
-			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;}
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=21; continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.a_mode=20+atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-gs")==0) {
-			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;}
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=25; continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.a_mode=24+atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-gq")==0) {
-			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;}
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=27; continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.a_mode=26+atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-gw")==0) {
-			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;}
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=72; continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.a_mode=71+atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-sample")==0) {
-		  if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.ni_subsample=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-eigen")==0) {
-			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;}
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=31; continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.a_mode=30+atoi(str.c_str());
-		}
-        else if (strcmp(argv[i], "-calccor")==0) {
-			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;}
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=71; continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.a_mode=70+atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-vc")==0) {
-			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;}
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=61; continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.a_mode=60+atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-ci")==0) {
-			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;}
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=66; continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.a_mode=65+atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-pve")==0) {
-		  double s=0;
-		  while (argv[i+1] != NULL && (argv[i+1][0] != '-' || !isalpha(argv[i+1][1]) ) ) {
-			  ++i;
-			  str.clear();
-			  str.assign(argv[i]);
-			  cPar.v_pve.push_back(atof(str.c_str()));
-			  s+=atof(str.c_str());
-			}
-			if (s==1) {
-			  cout<<"summation of pve equals one."<<endl;
-			}
-		}
-		else if (strcmp(argv[i], "-blocks")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.n_block=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-noconstrain")==0) {
-			cPar.noconstrain=true;
-		}
-		else if (strcmp(argv[i], "-lm")==0) {
-			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;}
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=51; continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.a_mode=50+atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-fa")==0 || strcmp(argv[i], "-lmm")==0) {
-			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;}
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=1; continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.a_mode=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-lmin")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.l_min=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-lmax")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.l_max=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-region")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.n_region=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-pnr")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.p_nr=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-emi")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.em_iter=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-nri")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.nr_iter=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-emp")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.em_prec=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-nrp")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.nr_prec=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-crt")==0) {
-			cPar.crt=1;
-		}
-		else if (strcmp(argv[i], "-bslmm")==0) {
-			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;}
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=11; continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.a_mode=10+atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-hmin")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.h_min=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-hmax")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.h_max=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-rmin")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.rho_min=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-rmax")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.rho_max=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-pmin")==0) {
-			if(argv[i+1] == NULL) {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.logp_min=atof(str.c_str())*log(10.0);
-		}
-		else if (strcmp(argv[i], "-pmax")==0) {
-			if(argv[i+1] == NULL) {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.logp_max=atof(str.c_str())*log(10.0);
-		}
-		else if (strcmp(argv[i], "-smin")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.s_min=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-smax")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.s_max=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-gmean")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.geo_mean=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-hscale")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.h_scale=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-rscale")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.rho_scale=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-pscale")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.logp_scale=atof(str.c_str())*log(10.0);
-		}
-		else if (strcmp(argv[i], "-w")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.w_step=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-s")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.s_step=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-rpace")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.r_pace=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-wpace")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.w_pace=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-seed")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.randseed=atol(str.c_str());
-		}
-		else if (strcmp(argv[i], "-mh")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.n_mh=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-predict")==0) {
-			if (cPar.a_mode!=0) {cPar.error=true; cout<<"error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm -predict -calccor options is allowed."<<endl; break;}
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {cPar.a_mode=41; continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.a_mode=40+atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-windowcm")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.window_cm=atof(str.c_str());
-		}
-		else if (strcmp(argv[i], "-windowbp")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.window_bp=atoi(str.c_str());
-		}
-		else if (strcmp(argv[i], "-windowns")==0) {
-			if(argv[i+1] == NULL || argv[i+1][0] == '-') {continue;}
-			++i;
-			str.clear();
-			str.assign(argv[i]);
-			cPar.window_ns=atoi(str.c_str());
-		}
-		else {cout<<"error! unrecognized option: "<<argv[i]<<endl; cPar.error=true; continue;}
-	}
-
-	// Change prediction mode to 43 if the epm file is not provided.
-	if (cPar.a_mode==41 && cPar.file_epm.empty()) {
-	  cPar.a_mode=43;
-	}
-
-	return;
+void GEMMA::Assign(int argc, char **argv, PARAM &cPar) {
+  string str;
+
+  for (int i = 1; i < argc; i++) {
+    if (strcmp(argv[i], "-bfile") == 0 || strcmp(argv[i], "--bfile") == 0 ||
+        strcmp(argv[i], "-b") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_bfile = str;
+    } else if (strcmp(argv[i], "-mbfile") == 0 ||
+               strcmp(argv[i], "--mbfile") == 0 ||
+               strcmp(argv[i], "-mb") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_mbfile = str;
+    } else if (strcmp(argv[i], "-silence") == 0) {
+      cPar.mode_silence = true;
+    } else if (strcmp(argv[i], "-g") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_geno = str;
+    } else if (strcmp(argv[i], "-mg") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_mgeno = str;
+    } else if (strcmp(argv[i], "-p") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_pheno = str;
+    } else if (strcmp(argv[i], "-a") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_anno = str;
+    }
+
+    // WJA added.
+    else if (strcmp(argv[i], "-oxford") == 0 ||
+             strcmp(argv[i], "--oxford") == 0 || strcmp(argv[i], "-x") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_oxford = str;
+    } else if (strcmp(argv[i], "-gxe") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_gxe = str;
+    } else if (strcmp(argv[i], "-widv") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_weight = str;
+    } else if (strcmp(argv[i], "-wsnp") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_wsnp = str;
+    } else if (strcmp(argv[i], "-wcat") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_wcat = str;
+    } else if (strcmp(argv[i], "-k") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_kin = str;
+    } else if (strcmp(argv[i], "-mk") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_mk = str;
+    } else if (strcmp(argv[i], "-u") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_ku = str;
+    } else if (strcmp(argv[i], "-d") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_kd = str;
+    } else if (strcmp(argv[i], "-c") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_cvt = str;
+    } else if (strcmp(argv[i], "-cat") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_cat = str;
+    } else if (strcmp(argv[i], "-mcat") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_mcat = str;
+    } else if (strcmp(argv[i], "-catc") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_catc = str;
+    } else if (strcmp(argv[i], "-mcatc") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_mcatc = str;
+    } else if (strcmp(argv[i], "-beta") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_beta = str;
+    } else if (strcmp(argv[i], "-bf") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_bf = str;
+    } else if (strcmp(argv[i], "-hyp") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_hyp = str;
+    } else if (strcmp(argv[i], "-cor") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_cor = str;
+    } else if (strcmp(argv[i], "-study") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_study = str;
+    } else if (strcmp(argv[i], "-ref") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_ref = str;
+    } else if (strcmp(argv[i], "-mstudy") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_mstudy = str;
+    } else if (strcmp(argv[i], "-mref") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_mref = str;
+    } else if (strcmp(argv[i], "-epm") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_epm = str;
+    } else if (strcmp(argv[i], "-en") == 0) {
+      while (argv[i + 1] != NULL && argv[i + 1][0] != '-') {
+        ++i;
+        str.clear();
+        str.assign(argv[i]);
+        cPar.est_column.push_back(atoi(str.c_str()));
+      }
+    } else if (strcmp(argv[i], "-ebv") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_ebv = str;
+    } else if (strcmp(argv[i], "-emu") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_log = str;
+    } else if (strcmp(argv[i], "-mu") == 0) {
+      if (argv[i + 1] == NULL) {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.pheno_mean = atof(str.c_str());
+    } else if (strcmp(argv[i], "-gene") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_gene = str;
+    } else if (strcmp(argv[i], "-r") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_read = str;
+    } else if (strcmp(argv[i], "-snps") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_snps = str;
+    } else if (strcmp(argv[i], "-km") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.k_mode = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-n") == 0) {
+      (cPar.p_column).clear();
+      while (argv[i + 1] != NULL && argv[i + 1][0] != '-') {
+        ++i;
+        str.clear();
+        str.assign(argv[i]);
+        (cPar.p_column).push_back(atoi(str.c_str()));
+      }
+    } else if (strcmp(argv[i], "-pace") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.d_pace = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-outdir") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.path_out = str;
+    } else if (strcmp(argv[i], "-o") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.file_out = str;
+    } else if (strcmp(argv[i], "-miss") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.miss_level = atof(str.c_str());
+    } else if (strcmp(argv[i], "-maf") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      if (cPar.maf_level != -1) {
+        cPar.maf_level = atof(str.c_str());
+      }
+    } else if (strcmp(argv[i], "-hwe") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.hwe_level = atof(str.c_str());
+    } else if (strcmp(argv[i], "-r2") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.r2_level = atof(str.c_str());
+    } else if (strcmp(argv[i], "-notsnp") == 0) {
+      cPar.maf_level = -1;
+    } else if (strcmp(argv[i], "-gk") == 0) {
+      if (cPar.a_mode != 0) {
+        cPar.error = true;
+        cout << "error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm "
+                "-predict -calccor options is allowed."
+             << endl;
+        break;
+      }
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        cPar.a_mode = 21;
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.a_mode = 20 + atoi(str.c_str());
+    } else if (strcmp(argv[i], "-gs") == 0) {
+      if (cPar.a_mode != 0) {
+        cPar.error = true;
+        cout << "error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm "
+                "-predict -calccor options is allowed."
+             << endl;
+        break;
+      }
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        cPar.a_mode = 25;
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.a_mode = 24 + atoi(str.c_str());
+    } else if (strcmp(argv[i], "-gq") == 0) {
+      if (cPar.a_mode != 0) {
+        cPar.error = true;
+        cout << "error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm "
+                "-predict -calccor options is allowed."
+             << endl;
+        break;
+      }
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        cPar.a_mode = 27;
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.a_mode = 26 + atoi(str.c_str());
+    } else if (strcmp(argv[i], "-gw") == 0) {
+      if (cPar.a_mode != 0) {
+        cPar.error = true;
+        cout << "error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm "
+                "-predict -calccor options is allowed."
+             << endl;
+        break;
+      }
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        cPar.a_mode = 72;
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.a_mode = 71 + atoi(str.c_str());
+    } else if (strcmp(argv[i], "-sample") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.ni_subsample = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-eigen") == 0) {
+      if (cPar.a_mode != 0) {
+        cPar.error = true;
+        cout << "error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm "
+                "-predict -calccor options is allowed."
+             << endl;
+        break;
+      }
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        cPar.a_mode = 31;
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.a_mode = 30 + atoi(str.c_str());
+    } else if (strcmp(argv[i], "-calccor") == 0) {
+      if (cPar.a_mode != 0) {
+        cPar.error = true;
+        cout << "error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm "
+                "-predict -calccor options is allowed."
+             << endl;
+        break;
+      }
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        cPar.a_mode = 71;
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.a_mode = 70 + atoi(str.c_str());
+    } else if (strcmp(argv[i], "-vc") == 0) {
+      if (cPar.a_mode != 0) {
+        cPar.error = true;
+        cout << "error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm "
+                "-predict -calccor options is allowed."
+             << endl;
+        break;
+      }
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        cPar.a_mode = 61;
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.a_mode = 60 + atoi(str.c_str());
+    } else if (strcmp(argv[i], "-ci") == 0) {
+      if (cPar.a_mode != 0) {
+        cPar.error = true;
+        cout << "error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm "
+                "-predict -calccor options is allowed."
+             << endl;
+        break;
+      }
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        cPar.a_mode = 66;
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.a_mode = 65 + atoi(str.c_str());
+    } else if (strcmp(argv[i], "-pve") == 0) {
+      double s = 0;
+      while (argv[i + 1] != NULL &&
+             (argv[i + 1][0] != '-' || !isalpha(argv[i + 1][1]))) {
+        ++i;
+        str.clear();
+        str.assign(argv[i]);
+        cPar.v_pve.push_back(atof(str.c_str()));
+        s += atof(str.c_str());
+      }
+      if (s == 1) {
+        cout << "summation of pve equals one." << endl;
+      }
+    } else if (strcmp(argv[i], "-blocks") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.n_block = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-noconstrain") == 0) {
+      cPar.noconstrain = true;
+    } else if (strcmp(argv[i], "-lm") == 0) {
+      if (cPar.a_mode != 0) {
+        cPar.error = true;
+        cout << "error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm "
+                "-predict -calccor options is allowed."
+             << endl;
+        break;
+      }
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        cPar.a_mode = 51;
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.a_mode = 50 + atoi(str.c_str());
+    } else if (strcmp(argv[i], "-fa") == 0 || strcmp(argv[i], "-lmm") == 0) {
+      if (cPar.a_mode != 0) {
+        cPar.error = true;
+        cout << "error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm "
+                "-predict -calccor options is allowed."
+             << endl;
+        break;
+      }
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        cPar.a_mode = 1;
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.a_mode = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-lmin") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.l_min = atof(str.c_str());
+    } else if (strcmp(argv[i], "-lmax") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.l_max = atof(str.c_str());
+    } else if (strcmp(argv[i], "-region") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.n_region = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-pnr") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.p_nr = atof(str.c_str());
+    } else if (strcmp(argv[i], "-emi") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.em_iter = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-nri") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.nr_iter = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-emp") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.em_prec = atof(str.c_str());
+    } else if (strcmp(argv[i], "-nrp") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.nr_prec = atof(str.c_str());
+    } else if (strcmp(argv[i], "-crt") == 0) {
+      cPar.crt = 1;
+    } else if (strcmp(argv[i], "-bslmm") == 0) {
+      if (cPar.a_mode != 0) {
+        cPar.error = true;
+        cout << "error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm "
+                "-predict -calccor options is allowed."
+             << endl;
+        break;
+      }
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        cPar.a_mode = 11;
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.a_mode = 10 + atoi(str.c_str());
+    } else if (strcmp(argv[i], "-hmin") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.h_min = atof(str.c_str());
+    } else if (strcmp(argv[i], "-hmax") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.h_max = atof(str.c_str());
+    } else if (strcmp(argv[i], "-rmin") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.rho_min = atof(str.c_str());
+    } else if (strcmp(argv[i], "-rmax") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.rho_max = atof(str.c_str());
+    } else if (strcmp(argv[i], "-pmin") == 0) {
+      if (argv[i + 1] == NULL) {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.logp_min = atof(str.c_str()) * log(10.0);
+    } else if (strcmp(argv[i], "-pmax") == 0) {
+      if (argv[i + 1] == NULL) {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.logp_max = atof(str.c_str()) * log(10.0);
+    } else if (strcmp(argv[i], "-smin") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.s_min = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-smax") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.s_max = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-gmean") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.geo_mean = atof(str.c_str());
+    } else if (strcmp(argv[i], "-hscale") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.h_scale = atof(str.c_str());
+    } else if (strcmp(argv[i], "-rscale") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.rho_scale = atof(str.c_str());
+    } else if (strcmp(argv[i], "-pscale") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.logp_scale = atof(str.c_str()) * log(10.0);
+    } else if (strcmp(argv[i], "-w") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.w_step = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-s") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.s_step = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-rpace") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.r_pace = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-wpace") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.w_pace = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-seed") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.randseed = atol(str.c_str());
+    } else if (strcmp(argv[i], "-mh") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.n_mh = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-predict") == 0) {
+      if (cPar.a_mode != 0) {
+        cPar.error = true;
+        cout << "error! only one of -gk -gs -eigen -vc -lm -lmm -bslmm "
+                "-predict -calccor options is allowed."
+             << endl;
+        break;
+      }
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        cPar.a_mode = 41;
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.a_mode = 40 + atoi(str.c_str());
+    } else if (strcmp(argv[i], "-windowcm") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.window_cm = atof(str.c_str());
+    } else if (strcmp(argv[i], "-windowbp") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.window_bp = atoi(str.c_str());
+    } else if (strcmp(argv[i], "-windowns") == 0) {
+      if (argv[i + 1] == NULL || argv[i + 1][0] == '-') {
+        continue;
+      }
+      ++i;
+      str.clear();
+      str.assign(argv[i]);
+      cPar.window_ns = atoi(str.c_str());
+    } else {
+      cout << "error! unrecognized option: " << argv[i] << endl;
+      cPar.error = true;
+      continue;
+    }
+  }
+
+  // Change prediction mode to 43 if the epm file is not provided.
+  if (cPar.a_mode == 41 && cPar.file_epm.empty()) {
+    cPar.a_mode = 43;
+  }
+
+  return;
 }
 
-void GEMMA::BatchRun (PARAM &cPar) {
-	clock_t time_begin, time_start;
-	time_begin=clock();
-
-	// Read Files.
-	cout<<"Reading Files ... "<<endl;
-	cPar.ReadFiles();
-	if (cPar.error==true) {cout<<"error! fail to read files. "<<endl; return;}
-	cPar.CheckData();
-	if (cPar.error==true) {cout<<"error! fail to check data. "<<endl; return;}
-
-	//Prediction for bslmm
-	if (cPar.a_mode==41 || cPar.a_mode==42) {
-		gsl_vector *y_prdt;
-
-		y_prdt=gsl_vector_alloc (cPar.ni_total-cPar.ni_test);
-
-		//set to zero
-		gsl_vector_set_zero (y_prdt);
-
-		PRDT cPRDT;
-		cPRDT.CopyFromParam(cPar);
-
-		//add breeding value if needed
-		if (!cPar.file_kin.empty() && !cPar.file_ebv.empty()) {
-			cout<<"Adding Breeding Values ... "<<endl;
-
-			gsl_matrix *G=gsl_matrix_alloc (cPar.ni_total, cPar.ni_total);
-			gsl_vector *u_hat=gsl_vector_alloc (cPar.ni_test);
-
-			//read kinship matrix and set u_hat
-			vector<int> indicator_all;
-			size_t c_bv=0;
-			for (size_t i=0; i<cPar.indicator_idv.size(); i++) {
-				indicator_all.push_back(1);
-				if (cPar.indicator_bv[i]==1) {gsl_vector_set(u_hat, c_bv, cPar.vec_bv[i]); c_bv++;}
-			}
-
-			ReadFile_kin (cPar.file_kin, indicator_all, cPar.mapID2num, cPar.k_mode, cPar.error, G);
-			if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;}
-
-			//read u
-			cPRDT.AddBV(G, u_hat, y_prdt);
-
-			gsl_matrix_free(G);
-			gsl_vector_free(u_hat);
-		}
-
-		//add beta
-		if (!cPar.file_bfile.empty()) {
-			cPRDT.AnalyzePlink (y_prdt);
-		}
-		else {
-			cPRDT.AnalyzeBimbam (y_prdt);
-		}
-
-		//add mu
-		gsl_vector_add_constant(y_prdt, cPar.pheno_mean);
-
-		//convert y to probability if needed
-		if (cPar.a_mode==42) {
-			double d;
-			for (size_t i=0; i<y_prdt->size; i++) {
-				d=gsl_vector_get(y_prdt, i);
-				d=gsl_cdf_gaussian_P(d, 1.0);
-				gsl_vector_set(y_prdt, i, d);
-			}
-		}
-
-
-		cPRDT.CopyToParam(cPar);
-
-		cPRDT.WriteFiles(y_prdt);
-
-		gsl_vector_free(y_prdt);
-	}
-
-	//Prediction with kinship matrix only; for one or more phenotypes
-	if (cPar.a_mode==43) {
-		//first, use individuals with full phenotypes to obtain estimates of Vg and Ve
-		gsl_matrix *Y=gsl_matrix_alloc (cPar.ni_test, cPar.n_ph);
-		gsl_matrix *W=gsl_matrix_alloc (Y->size1, cPar.n_cvt);
-		gsl_matrix *G=gsl_matrix_alloc (Y->size1, Y->size1);
-		gsl_matrix *U=gsl_matrix_alloc (Y->size1, Y->size1);
-		gsl_matrix *UtW=gsl_matrix_alloc (Y->size1, W->size2);
-		gsl_matrix *UtY=gsl_matrix_alloc (Y->size1, Y->size2);
-		gsl_vector *eval=gsl_vector_alloc (Y->size1);
-
-		gsl_matrix *Y_full=gsl_matrix_alloc (cPar.ni_cvt, cPar.n_ph);
-		gsl_matrix *W_full=gsl_matrix_alloc (Y_full->size1, cPar.n_cvt);
-
-		//set covariates matrix W and phenotype matrix Y
-		//an intercept should be included in W,
-		cPar.CopyCvtPhen (W, Y, 0);
-		cPar.CopyCvtPhen (W_full, Y_full, 1);
-
-		gsl_matrix *Y_hat=gsl_matrix_alloc (Y_full->size1, cPar.n_ph);
-		gsl_matrix *G_full=gsl_matrix_alloc (Y_full->size1, Y_full->size1);
-		gsl_matrix *H_full=gsl_matrix_alloc (Y_full->size1*Y_hat->size2, Y_full->size1*Y_hat->size2);
-
-		//read relatedness matrix G, and matrix G_full
-		ReadFile_kin (cPar.file_kin, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G);
-		if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;}
-		ReadFile_kin (cPar.file_kin, cPar.indicator_cvt, cPar.mapID2num, cPar.k_mode, cPar.error, G_full);
-		if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;}
-
-		//center matrix G
-		CenterMatrix (G);
-		CenterMatrix (G_full);
-
-		//eigen-decomposition and calculate trace_G
-		cout<<"Start Eigen-Decomposition..."<<endl;
-		time_start=clock();
-		cPar.trace_G=EigenDecomp (G, U, eval, 0);
-		cPar.trace_G=0.0;
-		for (size_t i=0; i<eval->size; i++) {
-			if (gsl_vector_get (eval, i)<1e-10) {gsl_vector_set (eval, i, 0);}
-			cPar.trace_G+=gsl_vector_get (eval, i);
-		}
-		cPar.trace_G/=(double)eval->size;
-		cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-
-		//calculate UtW and Uty
-		CalcUtX (U, W, UtW);
-		CalcUtX (U, Y, UtY);
-
-		//calculate variance component and beta estimates
-		//and then obtain predicted values
-		if (cPar.n_ph==1) {
-			gsl_vector *beta=gsl_vector_alloc (W->size2);
-			gsl_vector *se_beta=gsl_vector_alloc (W->size2);
-
-			double lambda, logl, vg, ve;
-			gsl_vector_view UtY_col=gsl_matrix_column (UtY, 0);
-
-			//obtain estimates
-			CalcLambda ('R', eval, UtW, &UtY_col.vector, cPar.l_min, cPar.l_max, cPar.n_region, lambda, logl);
-			CalcLmmVgVeBeta (eval, UtW, &UtY_col.vector, lambda, vg, ve, beta, se_beta);
-
-			cout<<"REMLE estimate for vg in the null model = "<<vg<<endl;
-			cout<<"REMLE estimate for ve in the null model = "<<ve<<endl;
-			cPar.vg_remle_null=vg; cPar.ve_remle_null=ve;
-
-			//obtain Y_hat from fixed effects
-			gsl_vector_view Yhat_col=gsl_matrix_column (Y_hat, 0);
-			gsl_blas_dgemv (CblasNoTrans, 1.0, W_full, beta, 0.0, &Yhat_col.vector);
-
-			//obtain H
-			gsl_matrix_set_identity (H_full);
-			gsl_matrix_scale (H_full, ve);
-			gsl_matrix_scale (G_full, vg);
-			gsl_matrix_add (H_full, G_full);
-
-			//free matrices
-			gsl_vector_free(beta);
-			gsl_vector_free(se_beta);
-		} else {
-			gsl_matrix *Vg=gsl_matrix_alloc (cPar.n_ph, cPar.n_ph);
-			gsl_matrix *Ve=gsl_matrix_alloc (cPar.n_ph, cPar.n_ph);
-			gsl_matrix *B=gsl_matrix_alloc (cPar.n_ph, W->size2);
-			gsl_matrix *se_B=gsl_matrix_alloc (cPar.n_ph, W->size2);
-
-			//obtain estimates
-			CalcMvLmmVgVeBeta (eval, UtW, UtY, cPar.em_iter, cPar.nr_iter, cPar.em_prec, cPar.nr_prec, cPar.l_min, cPar.l_max, cPar.n_region, Vg, Ve, B, se_B);
-
-			cout<<"REMLE estimate for Vg in the null model: "<<endl;
-			for (size_t i=0; i<Vg->size1; i++) {
-				for (size_t j=0; j<=i; j++) {
-					cout<<gsl_matrix_get(Vg, i, j)<<"\t";
-				}
-				cout<<endl;
-			}
-			cout<<"REMLE estimate for Ve in the null model: "<<endl;
-			for (size_t i=0; i<Ve->size1; i++) {
-				for (size_t j=0; j<=i; j++) {
-					cout<<gsl_matrix_get(Ve, i, j)<<"\t";
-				}
-				cout<<endl;
-			}
-			cPar.Vg_remle_null.clear();
-			cPar.Ve_remle_null.clear();
-			for (size_t i=0; i<Vg->size1; i++) {
-				for (size_t j=i; j<Vg->size2; j++) {
-					cPar.Vg_remle_null.push_back(gsl_matrix_get (Vg, i, j) );
-					cPar.Ve_remle_null.push_back(gsl_matrix_get (Ve, i, j) );
-				}
-			}
-
-			//obtain Y_hat from fixed effects
-			gsl_blas_dgemm (CblasNoTrans, CblasTrans, 1.0, W_full, B, 0.0, Y_hat);
-
-			//obtain H
-			KroneckerSym(G_full, Vg, H_full);
-			for (size_t i=0; i<G_full->size1; i++) {
-				gsl_matrix_view H_sub=gsl_matrix_submatrix (H_full, i*Ve->size1, i*Ve->size2, Ve->size1, Ve->size2);
-				gsl_matrix_add (&H_sub.matrix, Ve);
-			}
-
-			//free matrices
-			gsl_matrix_free (Vg);
-			gsl_matrix_free (Ve);
-			gsl_matrix_free (B);
-			gsl_matrix_free (se_B);
-		}
-
-		PRDT cPRDT;
-
-		cPRDT.CopyFromParam(cPar);
-
-		cout<<"Predicting Missing Phentypes ... "<<endl;
-		time_start=clock();
-		cPRDT.MvnormPrdt(Y_hat, H_full, Y_full);
-		cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-
-		cPRDT.WriteFiles(Y_full);
-
-		gsl_matrix_free(Y);
-		gsl_matrix_free(W);
-		gsl_matrix_free(G);
-		gsl_matrix_free(U);
-		gsl_matrix_free(UtW);
-		gsl_matrix_free(UtY);
-		gsl_vector_free(eval);
-
-		gsl_matrix_free(Y_full);
-		gsl_matrix_free(Y_hat);
-		gsl_matrix_free(W_full);
-		gsl_matrix_free(G_full);
-		gsl_matrix_free(H_full);
-	}
-
-
-	//Generate Kinship matrix
-	if (cPar.a_mode==21 || cPar.a_mode==22) {
-		cout<<"Calculating Relatedness Matrix ... "<<endl;
-
-		gsl_matrix *G=gsl_matrix_alloc (cPar.ni_total, cPar.ni_total);
-
-		time_start=clock();
-		cPar.CalcKin (G);
-		cPar.time_G=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-		if (cPar.error==true) {cout<<"error! fail to calculate relatedness matrix. "<<endl; return;}
-
-		if (cPar.a_mode==21) {
-			cPar.WriteMatrix (G, "cXX");
-		} else {
-			cPar.WriteMatrix (G, "sXX");
-		}
-
-		gsl_matrix_free (G);
-	}
-
-	//Compute the LDSC weights (not implemented yet)
-	if (cPar.a_mode==72) {
-		cout<<"Calculating Weights ... "<<endl;
-
-		VARCOV cVarcov;
-		cVarcov.CopyFromParam(cPar);
-
-		if (!cPar.file_bfile.empty()) {
-		  cVarcov.AnalyzePlink ();
-		} else {
-		  cVarcov.AnalyzeBimbam ();
-		}
-
-		cVarcov.CopyToParam(cPar);
-	}
-
-	// Compute the S matrix (and its variance), that is used for
-	// variance component estimation using summary statistics.
-	if (cPar.a_mode==25 || cPar.a_mode==26) {
-	  cout<<"Calculating the S Matrix ... "<<endl;
-
-	  gsl_matrix *S=gsl_matrix_alloc (cPar.n_vc*2, cPar.n_vc);
-	  gsl_vector *ns=gsl_vector_alloc (cPar.n_vc+1);
-	  gsl_matrix_set_zero(S);
-	  gsl_vector_set_zero(ns);
-
-	  gsl_matrix_view S_mat=gsl_matrix_submatrix(S, 0, 0, cPar.n_vc, cPar.n_vc);
-	  gsl_matrix_view Svar_mat=gsl_matrix_submatrix (S, cPar.n_vc, 0, cPar.n_vc, cPar.n_vc);
-	  gsl_vector_view ns_vec=gsl_vector_subvector(ns, 0, cPar.n_vc);
-
-	  gsl_matrix *K=gsl_matrix_alloc (cPar.ni_test, cPar.n_vc*cPar.ni_test);
-	  gsl_matrix *A=gsl_matrix_alloc (cPar.ni_test, cPar.n_vc*cPar.ni_test);
-	  gsl_matrix_set_zero (K);
-	  gsl_matrix_set_zero (A);
-
-	  gsl_vector *y=gsl_vector_alloc (cPar.ni_test);
-	  gsl_matrix *W=gsl_matrix_alloc (cPar.ni_test, cPar.n_cvt);
-
-	  cPar.CopyCvtPhen (W, y, 0);
-
-	  set<string> setSnps_beta;
-	  map <string, double> mapRS2wA, mapRS2wK;
-
-	  cPar.ObtainWeight(setSnps_beta, mapRS2wK);
-
-	  time_start=clock();
-	  cPar.CalcS (mapRS2wA, mapRS2wK, W, A, K, &S_mat.matrix, &Svar_mat.matrix, &ns_vec.vector);
-	  cPar.time_G=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-	  if (cPar.error==true) {cout<<"error! fail to calculate the S matrix. "<<endl; return;}
-
-	  gsl_vector_set (ns, cPar.n_vc, cPar.ni_test);
-
-	  cPar.WriteMatrix (S, "S");
-	  cPar.WriteVector (ns, "size");
-	  cPar.WriteVar ("snps");
-
-	  gsl_matrix_free (S);
-	  gsl_vector_free (ns);
-
-	  gsl_matrix_free (A);
-	  gsl_matrix_free (K);
-
-	  gsl_vector_free (y);
-	  gsl_matrix_free (K);
-	}
-
-	//Compute the q vector, that is used for variance component estimation using summary statistics
-	if (cPar.a_mode==27 || cPar.a_mode==28) {
-	  gsl_matrix *Vq=gsl_matrix_alloc (cPar.n_vc, cPar.n_vc);
-	  gsl_vector *q=gsl_vector_alloc (cPar.n_vc);
-	  gsl_vector *s=gsl_vector_alloc (cPar.n_vc+1);
-	  gsl_vector_set_zero (q);
-	  gsl_vector_set_zero (s);
-
-	  gsl_vector_view s_vec=gsl_vector_subvector(s, 0, cPar.n_vc);
-
-	  vector<size_t> vec_cat, vec_ni;
-	  vector<double> vec_weight, vec_z2;
-	  map<string, double> mapRS2weight;
-	  mapRS2weight.clear();
-
-	  time_start=clock();
-	  ReadFile_beta (cPar.file_beta, cPar.mapRS2cat, mapRS2weight, vec_cat, vec_ni, vec_weight, vec_z2, cPar.ni_total, cPar.ns_total, cPar.ns_test);
-	  cout<<"## number of total individuals = "<<cPar.ni_total<<endl;
-	  cout<<"## number of total SNPs = "<<cPar.ns_total<<endl;
-	  cout<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl;
-	  cout<<"## number of variance components = "<<cPar.n_vc<<endl;
-	  cout<<"Calculating the q vector ... "<<endl;
-	  Calcq (cPar.n_block, vec_cat, vec_ni, vec_weight, vec_z2, Vq, q, &s_vec.vector);
-	  cPar.time_G=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-
-	  if (cPar.error==true) {cout<<"error! fail to calculate the q vector. "<<endl; return;}
-
-	  gsl_vector_set (s, cPar.n_vc, cPar.ni_total);
-
-	  cPar.WriteMatrix (Vq, "Vq");
-	  cPar.WriteVector (q, "q");
-	  cPar.WriteVector (s, "size");
-	  /*
-	  for (size_t i=0; i<cPar.n_vc; i++) {
-	    cout<<gsl_vector_get(q, i)<<endl;
-	  }
-	  */
-	  gsl_matrix_free (Vq);
-	  gsl_vector_free (q);
-	  gsl_vector_free (s);
-	}
-
-	// Calculate SNP covariance.
-	if (cPar.a_mode==71) {
-	  VARCOV cVarcov;
-	  cVarcov.CopyFromParam(cPar);
-
-	  if (!cPar.file_bfile.empty()) {
-            cVarcov.AnalyzePlink ();
-	  } else {
-            cVarcov.AnalyzeBimbam ();
-	  }
-
-	  cVarcov.CopyToParam(cPar);
-	}
-
-	// LM.
-	if (cPar.a_mode==51 || cPar.a_mode==52 || cPar.a_mode==53 || cPar.a_mode==54) {  //Fit LM
-		gsl_matrix *Y=gsl_matrix_alloc (cPar.ni_test, cPar.n_ph);
-		gsl_matrix *W=gsl_matrix_alloc (Y->size1, cPar.n_cvt);
-
-		//set covariates matrix W and phenotype matrix Y
-		//an intercept should be included in W,
-		cPar.CopyCvtPhen (W, Y, 0);
-
-		//Fit LM or mvLM
-		if (cPar.n_ph==1) {
-			LM cLm;
-			cLm.CopyFromParam(cPar);
-
-			gsl_vector_view Y_col=gsl_matrix_column (Y, 0);
-
-			if (!cPar.file_gene.empty()) {
-				cLm.AnalyzeGene (W, &Y_col.vector); //y is the predictor, not the phenotype
-			} else if (!cPar.file_bfile.empty()) {
-				cLm.AnalyzePlink (W, &Y_col.vector);
-			} else if (!cPar.file_oxford.empty()) {
-				cLm.Analyzebgen (W, &Y_col.vector);
-			} else {
-				cLm.AnalyzeBimbam (W, &Y_col.vector);
-			}
-
-			cLm.WriteFiles();
-			cLm.CopyToParam(cPar);
-		}
-		/*
-		else {
-			MVLM cMvlm;
-			cMvlm.CopyFromParam(cPar);
-
-			if (!cPar.file_bfile.empty()) {
-				cMvlm.AnalyzePlink (W, Y);
-			} else {
-				cMvlm.AnalyzeBimbam (W, Y);
-			}
-
-			cMvlm.WriteFiles();
-			cMvlm.CopyToParam(cPar);
-		}
-		*/
-		//release all matrices and vectors
-		gsl_matrix_free (Y);
-		gsl_matrix_free (W);
-	}
-
-	//VC estimation with one or multiple kinship matrices
-	//REML approach only
-	//if file_kin or file_ku/kd is provided, then a_mode is changed to 5 already, in param.cpp
-	//for one phenotype only;
-	if (cPar.a_mode==61 || cPar.a_mode==62 || cPar.a_mode==63) {
-	  if (!cPar.file_beta.empty() ) {
-	    //need to obtain a common set of SNPs between beta file and the genotype file; these are saved in mapRS2wA and mapRS2wK
-	    //normalize the weight in mapRS2wK to have an average of one; each element of mapRS2wA is 1
-	    //update indicator_snps, so that the numbers are in accordance with mapRS2wK
-	    set<string> setSnps_beta;
-	    ReadFile_snps_header (cPar.file_beta, setSnps_beta);
-
-	    map <string, double> mapRS2wA, mapRS2wK;
-	    cPar.ObtainWeight(setSnps_beta, mapRS2wK);
-
-	    cPar.UpdateSNP (mapRS2wK);
-
-	    // Setup matrices and vectors.
-	    gsl_matrix *S=gsl_matrix_alloc (cPar.n_vc*2, cPar.n_vc);
-	    gsl_matrix *Vq=gsl_matrix_alloc (cPar.n_vc, cPar.n_vc);
-	    gsl_vector *q=gsl_vector_alloc (cPar.n_vc);
-	    gsl_vector *s=gsl_vector_alloc (cPar.n_vc+1);
-
-	    gsl_matrix *K=gsl_matrix_alloc (cPar.ni_test, cPar.n_vc*cPar.ni_test);
-	    gsl_matrix *A=gsl_matrix_alloc (cPar.ni_test, cPar.n_vc*cPar.ni_test);
-
-	    gsl_vector *y=gsl_vector_alloc (cPar.ni_test);
-	    gsl_matrix *W=gsl_matrix_alloc (cPar.ni_test, cPar.n_cvt);
-
-	    gsl_matrix_set_zero (K);
-	    gsl_matrix_set_zero (A);
-
-	    gsl_matrix_set_zero(S);
-	    gsl_matrix_set_zero(Vq);
-	    gsl_vector_set_zero (q);
-	    gsl_vector_set_zero (s);
-
-	    cPar.CopyCvtPhen (W, y, 0);
-
-	    gsl_matrix_view S_mat=gsl_matrix_submatrix(S, 0, 0, cPar.n_vc, cPar.n_vc);
-	    gsl_matrix_view Svar_mat=gsl_matrix_submatrix (S, cPar.n_vc, 0, cPar.n_vc, cPar.n_vc);
-	    gsl_vector_view s_vec=gsl_vector_subvector(s, 0, cPar.n_vc);
-
-	    vector<size_t> vec_cat, vec_ni;
-	    vector<double> vec_weight, vec_z2;
-
-	    //read beta, based on the mapRS2wK
-	    ReadFile_beta (cPar.file_beta, cPar.mapRS2cat, mapRS2wK, vec_cat, vec_ni, vec_weight, vec_z2, cPar.ni_study, cPar.ns_study, cPar.ns_test);
-
-	    cout<<"Study Panel: "<<endl;
-	    cout<<"## number of total individuals = "<<cPar.ni_study<<endl;
-	    cout<<"## number of total SNPs = "<<cPar.ns_study<<endl;
-	    cout<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl;
-	    cout<<"## number of variance components = "<<cPar.n_vc<<endl;
-
-	    //compute q
-	    Calcq (cPar.n_block, vec_cat, vec_ni, vec_weight, vec_z2, Vq, q, &s_vec.vector);
-
-	    //compute S
-	    time_start=clock();
-	    cPar.CalcS (mapRS2wA, mapRS2wK, W, A, K, &S_mat.matrix, &Svar_mat.matrix, &s_vec.vector);
-	    cPar.time_G+=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-	    if (cPar.error==true) {cout<<"error! fail to calculate the S matrix. "<<endl; return;}
-
-	    //compute vc estimates
-	    CalcVCss(Vq, &S_mat.matrix, &Svar_mat.matrix, q, &s_vec.vector, cPar.ni_study, cPar.v_pve, cPar.v_se_pve, cPar.pve_total, cPar.se_pve_total, cPar.v_sigma2, cPar.v_se_sigma2, cPar.v_enrich, cPar.v_se_enrich);
-
-	    //if LDSC weights, then compute the weights and run the above steps again
-	    if (cPar.a_mode==62) {
-	      //compute the weights and normalize the weights for A
-	      cPar.UpdateWeight (1, mapRS2wK, cPar.ni_study, &s_vec.vector, mapRS2wA);
-
-	      //read beta file again, and update weigths vector
-	      ReadFile_beta (cPar.file_beta, cPar.mapRS2cat, mapRS2wA, vec_cat, vec_ni, vec_weight, vec_z2, cPar.ni_study, cPar.ns_total, cPar.ns_test);
-
-	      //compute q
-	      Calcq (cPar.n_block, vec_cat, vec_ni, vec_weight, vec_z2, Vq, q, &s_vec.vector);
-
-	      //compute S
-	      time_start=clock();
-	      cPar.CalcS (mapRS2wA, mapRS2wK, W, A, K, &S_mat.matrix, &Svar_mat.matrix, &s_vec.vector);
-	      cPar.time_G+=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-	      if (cPar.error==true) {cout<<"error! fail to calculate the S matrix. "<<endl; return;}
-
-	      //compute vc estimates
-	      CalcVCss(Vq, &S_mat.matrix, &Svar_mat.matrix, q, &s_vec.vector, cPar.ni_study, cPar.v_pve, cPar.v_se_pve, cPar.pve_total, cPar.se_pve_total, cPar.v_sigma2, cPar.v_se_sigma2, cPar.v_enrich, cPar.v_se_enrich);
-	    }
-
-	    gsl_vector_set (s, cPar.n_vc, cPar.ni_test);
-
-	    cPar.WriteMatrix (S, "S");
-	    cPar.WriteMatrix (Vq, "Vq");
-	    cPar.WriteVector (q, "q");
-	    cPar.WriteVector (s, "size");
-
-	    gsl_matrix_free (S);
-	    gsl_matrix_free (Vq);
-	    gsl_vector_free (q);
-	    gsl_vector_free (s);
-
-	    gsl_matrix_free (A);
-	    gsl_matrix_free (K);
-	    gsl_vector_free (y);
-	    gsl_matrix_free (W);
-	  } else if (!cPar.file_study.empty() || !cPar.file_mstudy.empty()) {
-	    if (!cPar.file_study.empty()) {
-	      string sfile=cPar.file_study+".size.txt";
-	      CountFileLines (sfile, cPar.n_vc);
-	    } else {
-	      string file_name;
-	      igzstream infile (cPar.file_mstudy.c_str(), igzstream::in);
-	      if (!infile) {cout<<"error! fail to open mstudy file: "<<cPar.file_study<<endl; return;}
-
-	      safeGetline(infile, file_name);
-
-	      infile.clear();
-	      infile.close();
-
-	      string sfile=file_name+".size.txt";
-	      CountFileLines (sfile, cPar.n_vc);
-	    }
-
-	    cPar.n_vc=cPar.n_vc-1;
-
-	    gsl_matrix *S=gsl_matrix_alloc (2*cPar.n_vc, cPar.n_vc);
-	    gsl_matrix *Vq=gsl_matrix_alloc (cPar.n_vc, cPar.n_vc);
-	    //gsl_matrix *V=gsl_matrix_alloc (cPar.n_vc+1, (cPar.n_vc*(cPar.n_vc+1))/2*(cPar.n_vc+1) );
-	    //gsl_matrix *Vslope=gsl_matrix_alloc (n_lines+1, (n_lines*(n_lines+1))/2*(n_lines+1) );
-	    gsl_vector *q=gsl_vector_alloc (cPar.n_vc);
-	    gsl_vector *s_study=gsl_vector_alloc (cPar.n_vc);
-	    gsl_vector *s_ref=gsl_vector_alloc (cPar.n_vc);
-	    gsl_vector *s=gsl_vector_alloc (cPar.n_vc+1);
-
-	    gsl_matrix_set_zero(S);
-	    gsl_matrix_view S_mat=gsl_matrix_submatrix(S, 0, 0, cPar.n_vc, cPar.n_vc);
-	    gsl_matrix_view Svar_mat=gsl_matrix_submatrix (S, cPar.n_vc, 0, cPar.n_vc, cPar.n_vc);
-
-	    gsl_matrix_set_zero(Vq);
-	    //gsl_matrix_set_zero(V);
-	    //gsl_matrix_set_zero(Vslope);
-	    gsl_vector_set_zero(q);
-	    gsl_vector_set_zero(s_study);
-	    gsl_vector_set_zero(s_ref);
-
-	    if (!cPar.file_study.empty()) {
-	      ReadFile_study(cPar.file_study, Vq, q, s_study, cPar.ni_study);
-	    } else {
-	      ReadFile_mstudy(cPar.file_mstudy, Vq, q, s_study, cPar.ni_study);
-	    }
-
-	    if (!cPar.file_ref.empty()) {
-	      ReadFile_ref(cPar.file_ref, &S_mat.matrix, &Svar_mat.matrix, s_ref, cPar.ni_ref);
-	    } else {
-	      ReadFile_mref(cPar.file_mref, &S_mat.matrix, &Svar_mat.matrix, s_ref, cPar.ni_ref);
-	    }
-
-	    cout<<"## number of variance components = "<<cPar.n_vc<<endl;
-	    cout<<"## number of individuals in the sample = "<<cPar.ni_study<<endl;
-	    cout<<"## number of individuals in the reference = "<<cPar.ni_ref<<endl;
-
-	    CalcVCss(Vq, &S_mat.matrix, &Svar_mat.matrix, q, s_study, cPar.ni_study, cPar.v_pve, cPar.v_se_pve, cPar.pve_total, cPar.se_pve_total, cPar.v_sigma2, cPar.v_se_sigma2, cPar.v_enrich, cPar.v_se_enrich);
-
-	    gsl_vector_view s_sub=gsl_vector_subvector (s, 0, cPar.n_vc);
-	    gsl_vector_memcpy (&s_sub.vector, s_ref);
-	    gsl_vector_set (s, cPar.n_vc, cPar.ni_ref);
-
-	    cPar.WriteMatrix (S, "S");
-	    cPar.WriteMatrix (Vq, "Vq");
-	    cPar.WriteVector (q, "q");
-	    cPar.WriteVector (s, "size");
-
-	    gsl_matrix_free (S);
-	    gsl_matrix_free (Vq);
-	    //gsl_matrix_free (V);
-	    //gsl_matrix_free (Vslope);
-	    gsl_vector_free (q);
-	    gsl_vector_free (s_study);
-	    gsl_vector_free (s_ref);
-	    gsl_vector_free (s);
-	  } else {
-		gsl_matrix *Y=gsl_matrix_alloc (cPar.ni_test, cPar.n_ph);
-		gsl_matrix *W=gsl_matrix_alloc (Y->size1, cPar.n_cvt);
-		gsl_matrix *G=gsl_matrix_alloc (Y->size1, Y->size1*cPar.n_vc );
-
-		//set covariates matrix W and phenotype matrix Y
-		//an intercept should be included in W,
-		cPar.CopyCvtPhen (W, Y, 0);
-
-		//read kinship matrices
-		if (!(cPar.file_mk).empty()) {
-		  ReadFile_mk (cPar.file_mk, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G);
-		  if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;}
-
-		  //center matrix G, and obtain v_traceG
-		  double d=0;
-		  (cPar.v_traceG).clear();
-		  for (size_t i=0; i<cPar.n_vc; i++) {
-		    gsl_matrix_view G_sub=gsl_matrix_submatrix (G, 0, i*G->size1, G->size1, G->size1);
-		    CenterMatrix (&G_sub.matrix);
-		    d=0;
-		    for (size_t j=0; j<G->size1; j++) {
-		      d+=gsl_matrix_get (&G_sub.matrix, j, j);
-		    }
-		    d/=(double)G->size1;
-		    (cPar.v_traceG).push_back(d);
-		  }
-		} else if (!(cPar.file_kin).empty()) {
-			ReadFile_kin (cPar.file_kin, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G);
-			if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;}
-
-			//center matrix G
-			CenterMatrix (G);
-
-			(cPar.v_traceG).clear();
-			double d=0;
-			for (size_t j=0; j<G->size1; j++) {
-			  d+=gsl_matrix_get (G, j, j);
-			}
-			d/=(double)G->size1;
-			(cPar.v_traceG).push_back(d);
-		}
-			/*
-			//eigen-decomposition and calculate trace_G
-			cout<<"Start Eigen-Decomposition..."<<endl;
-			time_start=clock();
-
-			if (cPar.a_mode==31) {
-				cPar.trace_G=EigenDecomp (G, U, eval, 1);
-			} else {
-				cPar.trace_G=EigenDecomp (G, U, eval, 0);
-			}
-
-			cPar.trace_G=0.0;
-			for (size_t i=0; i<eval->size; i++) {
-				if (gsl_vector_get (eval, i)<1e-10) {gsl_vector_set (eval, i, 0);}
-				cPar.trace_G+=gsl_vector_get (eval, i);
-			}
-			cPar.trace_G/=(double)eval->size;
-
-			cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-		} else {
-			ReadFile_eigenU (cPar.file_ku, cPar.error, U);
-			if (cPar.error==true) {cout<<"error! fail to read the U file. "<<endl; return;}
-
-			ReadFile_eigenD (cPar.file_kd, cPar.error, eval);
-			if (cPar.error==true) {cout<<"error! fail to read the D file. "<<endl; return;}
-
-			cPar.trace_G=0.0;
-			for (size_t i=0; i<eval->size; i++) {
-				if (gsl_vector_get(eval, i)<1e-10) {gsl_vector_set(eval, i, 0);}
-			  	cPar.trace_G+=gsl_vector_get(eval, i);
-			}
-			cPar.trace_G/=(double)eval->size;
-		}
-		*/
-		//fit multiple variance components
-		if (cPar.n_ph==1) {
-		  //		  if (cPar.n_vc==1) {
-		    /*
-		    //calculate UtW and Uty
-		    CalcUtX (U, W, UtW);
-		    CalcUtX (U, Y, UtY);
-
-		    gsl_vector_view beta=gsl_matrix_row (B, 0);
-		    gsl_vector_view se_beta=gsl_matrix_row (se_B, 0);
-		    gsl_vector_view UtY_col=gsl_matrix_column (UtY, 0);
-
-		    CalcLambda ('L', eval, UtW, &UtY_col.vector, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_mle_null, cPar.logl_mle_H0);
-		    CalcLmmVgVeBeta (eval, UtW, &UtY_col.vector, cPar.l_mle_null, cPar.vg_mle_null, cPar.ve_mle_null, &beta.vector, &se_beta.vector);
-
-		    cPar.beta_mle_null.clear();
-		    cPar.se_beta_mle_null.clear();
-		    for (size_t i=0; i<B->size2; i++) {
-		      cPar.beta_mle_null.push_back(gsl_matrix_get(B, 0, i) );
-		      cPar.se_beta_mle_null.push_back(gsl_matrix_get(se_B, 0, i) );
-		    }
-
-		    CalcLambda ('R', eval, UtW, &UtY_col.vector, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_remle_null, cPar.logl_remle_H0);
-		    CalcLmmVgVeBeta (eval, UtW, &UtY_col.vector, cPar.l_remle_null, cPar.vg_remle_null, cPar.ve_remle_null, &beta.vector, &se_beta.vector);
-		    cPar.beta_remle_null.clear();
-		    cPar.se_beta_remle_null.clear();
-		    for (size_t i=0; i<B->size2; i++) {
-		      cPar.beta_remle_null.push_back(gsl_matrix_get(B, 0, i) );
-		      cPar.se_beta_remle_null.push_back(gsl_matrix_get(se_B, 0, i) );
-		    }
-
-		    CalcPve (eval, UtW, &UtY_col.vector, cPar.l_remle_null, cPar.trace_G, cPar.pve_null, cPar.pve_se_null);
-		    cPar.PrintSummary();
-
-		    //calculate and output residuals
-		    if (cPar.a_mode==5) {
-		      gsl_vector *Utu_hat=gsl_vector_alloc (Y->size1);
-		      gsl_vector *Ute_hat=gsl_vector_alloc (Y->size1);
-		      gsl_vector *u_hat=gsl_vector_alloc (Y->size1);
-		      gsl_vector *e_hat=gsl_vector_alloc (Y->size1);
-		      gsl_vector *y_hat=gsl_vector_alloc (Y->size1);
-
-		      //obtain Utu and Ute
-		      gsl_vector_memcpy (y_hat, &UtY_col.vector);
-		      gsl_blas_dgemv (CblasNoTrans, -1.0, UtW, &beta.vector, 1.0, y_hat);
-
-		      double d, u, e;
-		      for (size_t i=0; i<eval->size; i++) {
-			d=gsl_vector_get (eval, i);
-			u=cPar.l_remle_null*d/(cPar.l_remle_null*d+1.0)*gsl_vector_get(y_hat, i);
-			e=1.0/(cPar.l_remle_null*d+1.0)*gsl_vector_get(y_hat, i);
-			gsl_vector_set (Utu_hat, i, u);
-			gsl_vector_set (Ute_hat, i, e);
-		      }
-
-		      //obtain u and e
-		      gsl_blas_dgemv (CblasNoTrans, 1.0, U, Utu_hat, 0.0, u_hat);
-		      gsl_blas_dgemv (CblasNoTrans, 1.0, U, Ute_hat, 0.0, e_hat);
-
-		      //output residuals
-		      cPar.WriteVector(u_hat, "residU");
-		      cPar.WriteVector(e_hat, "residE");
-
-		      gsl_vector_free(u_hat);
-		      gsl_vector_free(e_hat);
-		      gsl_vector_free(y_hat);
-		    }
-*/
-		  //		  } else {
-		    gsl_vector_view Y_col=gsl_matrix_column (Y, 0);
-		    VC cVc;
-		    cVc.CopyFromParam(cPar);
-		    if (cPar.a_mode==61) {
-		      cVc.CalcVChe (G, W, &Y_col.vector);
-		    } else if (cPar.a_mode==62) {
-		      cVc.CalcVCreml (cPar.noconstrain, G, W, &Y_col.vector);
-		    } else {
-		      cVc.CalcVCacl (G, W, &Y_col.vector);
-		    }
-		    cVc.CopyToParam(cPar);
-		    //obtain pve from sigma2
-		    //obtain se_pve from se_sigma2
-
-		    //}
-		}
-	  }
-
-	}
-
-
-	//compute confidence intervals with additional summary statistics
-	//we do not check the sign of z-scores here, but they have to be matched with the genotypes
-	if (cPar.a_mode==66 || cPar.a_mode==67) {
-	  //read reference file first
-	  gsl_matrix *S=gsl_matrix_alloc (cPar.n_vc, cPar.n_vc);
-	  gsl_matrix *Svar=gsl_matrix_alloc (cPar.n_vc, cPar.n_vc);
-	  gsl_vector *s_ref=gsl_vector_alloc (cPar.n_vc);
-
-	  gsl_matrix_set_zero(S);
-	  gsl_matrix_set_zero(Svar);
-	  gsl_vector_set_zero(s_ref);
-
-	  if (!cPar.file_ref.empty()) {
-	    ReadFile_ref(cPar.file_ref, S, Svar, s_ref, cPar.ni_ref);
-	  } else {
-	    ReadFile_mref(cPar.file_mref, S, Svar, s_ref, cPar.ni_ref);
-	  }
-
-	  //need to obtain a common set of SNPs between beta file and the genotype file; these are saved in mapRS2wA and mapRS2wK
-	  //normalize the weight in mapRS2wK to have an average of one; each element of mapRS2wA is 1
-	  set<string> setSnps_beta;
-	  ReadFile_snps_header (cPar.file_beta, setSnps_beta);
-
-	  //obtain the weights for wA, which contains the SNP weights for SNPs used in the model
-	  map <string, double> mapRS2wK;
-	  cPar.ObtainWeight(setSnps_beta, mapRS2wK);
-
-	  //set up matrices and vector
-	  gsl_matrix *Xz=gsl_matrix_alloc (cPar.ni_test, cPar.n_vc);
-	  gsl_matrix *XWz=gsl_matrix_alloc (cPar.ni_test, cPar.n_vc);
-	  gsl_matrix *XtXWz=gsl_matrix_alloc (mapRS2wK.size(), cPar.n_vc*cPar.n_vc);
-	  gsl_vector *w=gsl_vector_alloc (mapRS2wK.size());
-	  gsl_vector *w1=gsl_vector_alloc (mapRS2wK.size());
-	  gsl_vector *z=gsl_vector_alloc (mapRS2wK.size());
-	  gsl_vector *s_vec=gsl_vector_alloc (cPar.n_vc);
-
-	  vector<size_t> vec_cat, vec_size;
-	  vector<double> vec_z;
-
-	  map <string, double> mapRS2z, mapRS2wA;
-	  map <string, string> mapRS2A1;
-	  string file_str;
-
-	  //update s_vec, the number of snps in each category
-	  for (size_t i=0; i<cPar.n_vc; i++) {
-	    vec_size.push_back(0);
-	  }
-
-	  for (map<string, double>::const_iterator it=mapRS2wK.begin(); it!=mapRS2wK.end(); ++it) {
-	    vec_size[cPar.mapRS2cat[it->first]]++;
-	  }
-
-	  for (size_t i=0; i<cPar.n_vc; i++) {
-	    gsl_vector_set(s_vec, i, vec_size[i]);
-	  }
-
-	  //update mapRS2wA using v_pve and s_vec
-	  if (cPar.a_mode==66) {
-	    for (map<string, double>::const_iterator it=mapRS2wK.begin(); it!=mapRS2wK.end(); ++it) {
-	      mapRS2wA[it->first]=1;
-	    }
-	  } else {
-	    cPar.UpdateWeight (0, mapRS2wK, cPar.ni_test, s_vec, mapRS2wA);
-	  }
-
-	  //read in z-scores based on allele 0, and save that into a vector
-	  ReadFile_beta (cPar.file_beta, mapRS2wA, mapRS2A1, mapRS2z);
-
-	  //update snp indicator, save weights to w, save z-scores to vec_z, save category label to vec_cat
-	  //sign of z is determined by matching alleles
-	  cPar.UpdateSNPnZ (mapRS2wA, mapRS2A1, mapRS2z, w, z, vec_cat);
-
-	  //compute an n by k matrix of X_iWz
-	  cout<<"Calculating Xz ... "<<endl;
-
-	  gsl_matrix_set_zero(Xz);
-	  gsl_vector_set_all (w1, 1);
-
-	  if (!cPar.file_bfile.empty() ) {
-	    file_str=cPar.file_bfile+".bed";
-	    PlinkXwz (file_str, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp, vec_cat, w1, z, 0, Xz);
-	  } else if (!cPar.file_geno.empty()) {
-	    BimbamXwz (cPar.file_geno, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp, vec_cat, w1, z, 0, Xz);
-	  } else if (!cPar.file_mbfile.empty() ){
-	    MFILEXwz (1, cPar.file_mbfile, cPar.d_pace, cPar.indicator_idv, cPar.mindicator_snp, vec_cat, w1, z, Xz);
-	  } else if (!cPar.file_mgeno.empty()) {
-	    MFILEXwz (0, cPar.file_mgeno, cPar.d_pace, cPar.indicator_idv, cPar.mindicator_snp, vec_cat, w1, z, Xz);
-	  }
-	  /*
-	  cout<<"Xz: "<<endl;
-	  for (size_t i=0; i<5; i++) {
-	    for (size_t j=0; j<cPar.n_vc; j++) {
-	      cout<<gsl_matrix_get (Xz, i, j)<<" ";
-	    }
-	    cout<<endl;
-	  }
-	  */
-	  if (cPar.a_mode==66) {
-	    gsl_matrix_memcpy (XWz, Xz);
-	  } else if (cPar.a_mode==67) {
-	    cout<<"Calculating XWz ... "<<endl;
-
-	    gsl_matrix_set_zero(XWz);
-
-	    if (!cPar.file_bfile.empty() ) {
-	      file_str=cPar.file_bfile+".bed";
-	      PlinkXwz (file_str, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp, vec_cat, w, z, 0, XWz);
-	    } else if (!cPar.file_geno.empty()) {
-	      BimbamXwz (cPar.file_geno, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp, vec_cat, w, z, 0, XWz);
-	    } else if (!cPar.file_mbfile.empty() ){
-	      MFILEXwz (1, cPar.file_mbfile, cPar.d_pace, cPar.indicator_idv, cPar.mindicator_snp, vec_cat, w, z, XWz);
-	    } else if (!cPar.file_mgeno.empty()) {
-	      MFILEXwz (0, cPar.file_mgeno, cPar.d_pace, cPar.indicator_idv, cPar.mindicator_snp, vec_cat, w, z, XWz);
-	    }
-	  }
-	  /*
-	  cout<<"XWz: "<<endl;
-	  for (size_t i=0; i<5; i++) {
-	    cout<<gsl_vector_get (w, i)<<endl;
-	    for (size_t j=0; j<cPar.n_vc; j++) {
-	      cout<<gsl_matrix_get (XWz, i, j)<<" ";
-	    }
-	    cout<<endl;
-	  }
-	  */
-	  //compute an p by k matrix of X_j^TWX_iWz
-	  cout<<"Calculating XtXWz ... "<<endl;
-	  gsl_matrix_set_zero(XtXWz);
-
-	  if (!cPar.file_bfile.empty() ) {
-	    file_str=cPar.file_bfile+".bed";
-	    PlinkXtXwz (file_str, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp, XWz, 0, XtXWz);
-	  } else if (!cPar.file_geno.empty()) {
-	    BimbamXtXwz (cPar.file_geno, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp, XWz, 0, XtXWz);
-	  } else if (!cPar.file_mbfile.empty() ){
-	    MFILEXtXwz (1, cPar.file_mbfile, cPar.d_pace, cPar.indicator_idv, cPar.mindicator_snp, XWz, XtXWz);
-	  } else if (!cPar.file_mgeno.empty()) {
-	    MFILEXtXwz (0, cPar.file_mgeno, cPar.d_pace, cPar.indicator_idv, cPar.mindicator_snp, XWz, XtXWz);
-	  }
-	  /*
-	  cout<<"XtXWz: "<<endl;
-	  for (size_t i=0; i<5; i++) {
-	    for (size_t j=0; j<cPar.n_vc; j++) {
-	      cout<<gsl_matrix_get (XtXWz, i, j)<<" ";
-	    }
-	    cout<<endl;
-	  }
-	  */
-	  //compute confidence intervals
-	  CalcCIss(Xz, XWz, XtXWz, S, Svar, w, z, s_vec, vec_cat, cPar.v_pve, cPar.v_se_pve, cPar.pve_total, cPar.se_pve_total, cPar.v_sigma2, cPar.v_se_sigma2, cPar.v_enrich, cPar.v_se_enrich);
-
-	  //write files
-	  //cPar.WriteMatrix (XWz, "XWz");
-	  //cPar.WriteMatrix (XtXWz, "XtXWz");
-	  //cPar.WriteVector (w, "w");
-
-	  gsl_matrix_free(S);
-	  gsl_matrix_free(Svar);
-	  gsl_vector_free(s_ref);
-
-	  gsl_matrix_free(Xz);
-	  gsl_matrix_free(XWz);
-	  gsl_matrix_free(XtXWz);
-	  gsl_vector_free(w);
-	  gsl_vector_free(w1);
-	  gsl_vector_free(z);
-	  gsl_vector_free(s_vec);
-	}
-
-
-	//LMM or mvLMM or Eigen-Decomposition
-	if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4 || cPar.a_mode==5 || cPar.a_mode==31) {  //Fit LMM or mvLMM or eigen
-		gsl_matrix *Y=gsl_matrix_alloc (cPar.ni_test, cPar.n_ph);
-		gsl_matrix *W=gsl_matrix_alloc (Y->size1, cPar.n_cvt);
-		gsl_matrix *B=gsl_matrix_alloc (Y->size2, W->size2);	//B is a d by c matrix
-		gsl_matrix *se_B=gsl_matrix_alloc (Y->size2, W->size2);
-		gsl_matrix *G=gsl_matrix_alloc (Y->size1, Y->size1);
-		gsl_matrix *U=gsl_matrix_alloc (Y->size1, Y->size1);
-		gsl_matrix *UtW=gsl_matrix_alloc (Y->size1, W->size2);
-		gsl_matrix *UtY=gsl_matrix_alloc (Y->size1, Y->size2);
-		gsl_vector *eval=gsl_vector_alloc (Y->size1);
-		gsl_vector *env=gsl_vector_alloc (Y->size1);
-		gsl_vector *weight=gsl_vector_alloc (Y->size1);
-
-		//set covariates matrix W and phenotype matrix Y
-		//an intercept should be included in W,
-		cPar.CopyCvtPhen (W, Y, 0);
-		if (!cPar.file_gxe.empty()) {cPar.CopyGxe (env);}
-
-		//read relatedness matrix G
-		if (!(cPar.file_kin).empty()) {
-			ReadFile_kin (cPar.file_kin, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G);
-			if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;}
-
-			//center matrix G
-			CenterMatrix (G);
-
-			//is residual weights are provided, then
-			if (!cPar.file_weight.empty()) {
-			  cPar.CopyWeight (weight);
-			  double d, wi, wj;
-			  for (size_t i=0; i<G->size1; i++) {
-			    wi=gsl_vector_get(weight, i);
-			    for (size_t j=i; j<G->size2; j++) {
-			      wj=gsl_vector_get(weight, j);
-			      d=gsl_matrix_get(G, i, j);
-			      if (wi<=0 || wj<=0) {d=0;} else {d/=sqrt(wi*wj);}
-			      gsl_matrix_set(G, i, j, d);
-			      if (j!=i) {gsl_matrix_set(G, j, i, d);}
-			    }
-			  }
-			}
-
-			//eigen-decomposition and calculate trace_G
-			cout<<"Start Eigen-Decomposition..."<<endl;
-			time_start=clock();
-
-			if (cPar.a_mode==31) {
-				cPar.trace_G=EigenDecomp (G, U, eval, 1);
-			} else {
-				cPar.trace_G=EigenDecomp (G, U, eval, 0);
-			}
-
-			if (!cPar.file_weight.empty()) {
-			  double wi;
-			  for (size_t i=0; i<U->size1; i++) {
-			    wi=gsl_vector_get(weight, i);
-			    if (wi<=0) {wi=0;} else {wi=sqrt(wi);}
-			    gsl_vector_view Urow=gsl_matrix_row (U, i);
-			    gsl_vector_scale (&Urow.vector, wi);
-			  }
-			}
-
-			cPar.trace_G=0.0;
-			for (size_t i=0; i<eval->size; i++) {
-				if (gsl_vector_get (eval, i)<1e-10) {gsl_vector_set (eval, i, 0);}
-				cPar.trace_G+=gsl_vector_get (eval, i);
-			}
-			cPar.trace_G/=(double)eval->size;
-
-			cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-		} else {
-			ReadFile_eigenU (cPar.file_ku, cPar.error, U);
-			if (cPar.error==true) {cout<<"error! fail to read the U file. "<<endl; return;}
-
-			ReadFile_eigenD (cPar.file_kd, cPar.error, eval);
-			if (cPar.error==true) {cout<<"error! fail to read the D file. "<<endl; return;}
-
-			cPar.trace_G=0.0;
-			for (size_t i=0; i<eval->size; i++) {
-				if (gsl_vector_get(eval, i)<1e-10) {gsl_vector_set(eval, i, 0);}
-			  	cPar.trace_G+=gsl_vector_get(eval, i);
-			}
-			cPar.trace_G/=(double)eval->size;
-		}
-
-		if (cPar.a_mode==31) {
-			cPar.WriteMatrix(U, "eigenU");
-			cPar.WriteVector(eval, "eigenD");
-		} else if (!cPar.file_gene.empty() ) {
-			//calculate UtW and Uty
-			CalcUtX (U, W, UtW);
-			CalcUtX (U, Y, UtY);
-
-			LMM cLmm;
-			cLmm.CopyFromParam(cPar);
-
-			gsl_vector_view Y_col=gsl_matrix_column (Y, 0);
-			gsl_vector_view UtY_col=gsl_matrix_column (UtY, 0);
-
-			cLmm.AnalyzeGene (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector); //y is the predictor, not the phenotype
-
-			cLmm.WriteFiles();
-			cLmm.CopyToParam(cPar);
-		} else {
-		  //calculate UtW and Uty
-		  CalcUtX (U, W, UtW);
-		  CalcUtX (U, Y, UtY);
-
-			//calculate REMLE/MLE estimate and pve for univariate model
-			if (cPar.n_ph==1) {
-				gsl_vector_view beta=gsl_matrix_row (B, 0);
-				gsl_vector_view se_beta=gsl_matrix_row (se_B, 0);
-				gsl_vector_view UtY_col=gsl_matrix_column (UtY, 0);
-
-				CalcLambda ('L', eval, UtW, &UtY_col.vector, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_mle_null, cPar.logl_mle_H0);
-				CalcLmmVgVeBeta (eval, UtW, &UtY_col.vector, cPar.l_mle_null, cPar.vg_mle_null, cPar.ve_mle_null, &beta.vector, &se_beta.vector);
-
-				cPar.beta_mle_null.clear();
-				cPar.se_beta_mle_null.clear();
-				for (size_t i=0; i<B->size2; i++) {
-					cPar.beta_mle_null.push_back(gsl_matrix_get(B, 0, i) );
-					cPar.se_beta_mle_null.push_back(gsl_matrix_get(se_B, 0, i) );
-				}
-
-				CalcLambda ('R', eval, UtW, &UtY_col.vector, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_remle_null, cPar.logl_remle_H0);
-				CalcLmmVgVeBeta (eval, UtW, &UtY_col.vector, cPar.l_remle_null, cPar.vg_remle_null, cPar.ve_remle_null, &beta.vector, &se_beta.vector);
-				cPar.beta_remle_null.clear();
-				cPar.se_beta_remle_null.clear();
-				for (size_t i=0; i<B->size2; i++) {
-					cPar.beta_remle_null.push_back(gsl_matrix_get(B, 0, i) );
-					cPar.se_beta_remle_null.push_back(gsl_matrix_get(se_B, 0, i) );
-				}
-
-				CalcPve (eval, UtW, &UtY_col.vector, cPar.l_remle_null, cPar.trace_G, cPar.pve_null, cPar.pve_se_null);
-				cPar.PrintSummary();
-
-				//calculate and output residuals
-				if (cPar.a_mode==5) {
-					gsl_vector *Utu_hat=gsl_vector_alloc (Y->size1);
-					gsl_vector *Ute_hat=gsl_vector_alloc (Y->size1);
-					gsl_vector *u_hat=gsl_vector_alloc (Y->size1);
-					gsl_vector *e_hat=gsl_vector_alloc (Y->size1);
-					gsl_vector *y_hat=gsl_vector_alloc (Y->size1);
-
-					//obtain Utu and Ute
-					gsl_vector_memcpy (y_hat, &UtY_col.vector);
-					gsl_blas_dgemv (CblasNoTrans, -1.0, UtW, &beta.vector, 1.0, y_hat);
-
-					double d, u, e;
-					for (size_t i=0; i<eval->size; i++) {
-						d=gsl_vector_get (eval, i);
-						u=cPar.l_remle_null*d/(cPar.l_remle_null*d+1.0)*gsl_vector_get(y_hat, i);
-						e=1.0/(cPar.l_remle_null*d+1.0)*gsl_vector_get(y_hat, i);
-						gsl_vector_set (Utu_hat, i, u);
-						gsl_vector_set (Ute_hat, i, e);
-					}
-
-					//obtain u and e
-					gsl_blas_dgemv (CblasNoTrans, 1.0, U, Utu_hat, 0.0, u_hat);
-					gsl_blas_dgemv (CblasNoTrans, 1.0, U, Ute_hat, 0.0, e_hat);
-
-					//output residuals
-					cPar.WriteVector(u_hat, "residU");
-					cPar.WriteVector(e_hat, "residE");
-
-					gsl_vector_free(u_hat);
-					gsl_vector_free(e_hat);
-					gsl_vector_free(y_hat);
-				}
-			}
-
-			//Fit LMM or mvLMM
-			if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4) {
-				if (cPar.n_ph==1) {
-					LMM cLmm;
-					cLmm.CopyFromParam(cPar);
-
-					gsl_vector_view Y_col=gsl_matrix_column (Y, 0);
-					gsl_vector_view UtY_col=gsl_matrix_column (UtY, 0);
-
-					if (!cPar.file_bfile.empty()) {
-					  if (cPar.file_gxe.empty()) {
-					    cLmm.AnalyzePlink (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector);
-					  } else {
-					    cLmm.AnalyzePlinkGXE (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector, env);
-					  }
-					}
-					// WJA added
-				       	else if(!cPar.file_oxford.empty()) {
-					  cLmm.Analyzebgen (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector);
-					}
-					else {
-					  if (cPar.file_gxe.empty()) {
-					    cLmm.AnalyzeBimbam (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector);
-					  } else {
-					    cLmm.AnalyzeBimbamGXE (U, eval, UtW, &UtY_col.vector, W, &Y_col.vector, env);
-					  }
-					}
-
-					cLmm.WriteFiles();
-					cLmm.CopyToParam(cPar);
-				} else {
-					MVLMM cMvlmm;
-					cMvlmm.CopyFromParam(cPar);
-
-					if (!cPar.file_bfile.empty()) {
-					  if (cPar.file_gxe.empty()) {
-					    cMvlmm.AnalyzePlink (U, eval, UtW, UtY);
-					  } else {
-					    cMvlmm.AnalyzePlinkGXE (U, eval, UtW, UtY, env);
-					  }
-					}
-					else if(!cPar.file_oxford.empty())
-					{
-					    cMvlmm.Analyzebgen (U, eval, UtW, UtY);
-					}
-					else {
-					  if (cPar.file_gxe.empty()) {
-					    cMvlmm.AnalyzeBimbam (U, eval, UtW, UtY);
-					  } else {
-					    cMvlmm.AnalyzeBimbamGXE (U, eval, UtW, UtY, env);
-					  }
-					}
-
-					cMvlmm.WriteFiles();
-					cMvlmm.CopyToParam(cPar);
-				}
-			}
-		}
-
-
-		//release all matrices and vectors
-		gsl_matrix_free (Y);
-		gsl_matrix_free (W);
-		gsl_matrix_free(B);
-		gsl_matrix_free(se_B);
-		gsl_matrix_free (G);
-		gsl_matrix_free (U);
-		gsl_matrix_free (UtW);
-		gsl_matrix_free (UtY);
-		gsl_vector_free (eval);
-		gsl_vector_free (env);
-	}
-
-
-	//BSLMM
-	if (cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) {
-		gsl_vector *y=gsl_vector_alloc (cPar.ni_test);
-		gsl_matrix *W=gsl_matrix_alloc (y->size, cPar.n_cvt);
-		gsl_matrix *G=gsl_matrix_alloc (y->size, y->size);
-		gsl_matrix *UtX=gsl_matrix_alloc (y->size, cPar.ns_test);
-
-		//set covariates matrix W and phenotype vector y
-		//an intercept should be included in W,
-		cPar.CopyCvtPhen (W, y, 0);
-
-		//center y, even for case/control data
-		cPar.pheno_mean=CenterVector(y);
-
-		//run bvsr if rho==1
-		if (cPar.rho_min==1 && cPar.rho_max==1) {
-		  //read genotypes X (not UtX)
-		  cPar.ReadGenotypes (UtX, G, false);
-
-		  //perform BSLMM analysis
-		  BSLMM cBslmm;
-		  cBslmm.CopyFromParam(cPar);
-		  time_start=clock();
-		  cBslmm.MCMC(UtX, y);
-		  cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-		  cBslmm.CopyToParam(cPar);
-		  //else, if rho!=1
-		} else {
-		gsl_matrix *U=gsl_matrix_alloc (y->size, y->size);
-		gsl_vector *eval=gsl_vector_alloc (y->size);
-		gsl_matrix *UtW=gsl_matrix_alloc (y->size, W->size2);
-		gsl_vector *Uty=gsl_vector_alloc (y->size);
-
-		//read relatedness matrix G
-		if (!(cPar.file_kin).empty()) {
-			cPar.ReadGenotypes (UtX, G, false);
-
-			//read relatedness matrix G
-			ReadFile_kin (cPar.file_kin, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G);
-			if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;}
-
-			//center matrix G
-			CenterMatrix (G);
-		} else {
-			cPar.ReadGenotypes (UtX, G, true);
-		}
-
-		//eigen-decomposition and calculate trace_G
-		cout<<"Start Eigen-Decomposition..."<<endl;
-		time_start=clock();
-		cPar.trace_G=EigenDecomp (G, U, eval, 0);
-		cPar.trace_G=0.0;
-		for (size_t i=0; i<eval->size; i++) {
-			if (gsl_vector_get (eval, i)<1e-10) {gsl_vector_set (eval, i, 0);}
-			cPar.trace_G+=gsl_vector_get (eval, i);
-		}
-		cPar.trace_G/=(double)eval->size;
-		cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-
-		//calculate UtW and Uty
-		CalcUtX (U, W, UtW);
-		CalcUtX (U, y, Uty);
-
-		//calculate REMLE/MLE estimate and pve
-		CalcLambda ('L', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_mle_null, cPar.logl_mle_H0);
-		CalcLambda ('R', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_remle_null, cPar.logl_remle_H0);
-		CalcPve (eval, UtW, Uty, cPar.l_remle_null, cPar.trace_G, cPar.pve_null, cPar.pve_se_null);
-
-		cPar.PrintSummary();
-
-		//Creat and calcualte UtX, use a large memory
-		cout<<"Calculating UtX..."<<endl;
-		time_start=clock();
-		CalcUtX (U, UtX);
-		cPar.time_UtX=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-
-		//perform BSLMM or BSLMMDAP analysis
-		if (cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) {
-		  BSLMM cBslmm;
-		  cBslmm.CopyFromParam(cPar);
-		  time_start=clock();
-		  if (cPar.a_mode==12) {  //ridge regression
-		    cBslmm.RidgeR(U, UtX, Uty, eval, cPar.l_remle_null);
-		  } else {	//Run MCMC
-		    cBslmm.MCMC(U, UtX, Uty, eval, y);
-		  }
-		  cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-		  cBslmm.CopyToParam(cPar);
-		} else {
-		}
-
-		//release all matrices and vectors
-		gsl_matrix_free (G);
-		gsl_matrix_free (U);
-		gsl_matrix_free (UtW);
-		gsl_vector_free (eval);
-		gsl_vector_free (Uty);
-
-		}
-		gsl_matrix_free (W);
-		gsl_vector_free (y);
-		gsl_matrix_free (UtX);
-	}
-
-
-
-	//BSLMM-DAP
-	if (cPar.a_mode==14 || cPar.a_mode==15 || cPar.a_mode==16) {
-	  if (cPar.a_mode==14) {
-	    gsl_vector *y=gsl_vector_alloc (cPar.ni_test);
-	    gsl_matrix *W=gsl_matrix_alloc (y->size, cPar.n_cvt);
-	    gsl_matrix *G=gsl_matrix_alloc (y->size, y->size);
-	    gsl_matrix *UtX=gsl_matrix_alloc (y->size, cPar.ns_test);
-
-	    //set covariates matrix W and phenotype vector y
-	    //an intercept should be included in W,
-	    cPar.CopyCvtPhen (W, y, 0);
-
-	    //center y, even for case/control data
-	    cPar.pheno_mean=CenterVector(y);
-
-	    //run bvsr if rho==1
-	    if (cPar.rho_min==1 && cPar.rho_max==1) {
-	      //read genotypes X (not UtX)
-	      cPar.ReadGenotypes (UtX, G, false);
-
-	      //perform BSLMM analysis
-	      BSLMM cBslmm;
-	      cBslmm.CopyFromParam(cPar);
-	      time_start=clock();
-	      cBslmm.MCMC(UtX, y);
-	      cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-	      cBslmm.CopyToParam(cPar);
-	      //else, if rho!=1
-	    } else {
-	      gsl_matrix *U=gsl_matrix_alloc (y->size, y->size);
-	      gsl_vector *eval=gsl_vector_alloc (y->size);
-	      gsl_matrix *UtW=gsl_matrix_alloc (y->size, W->size2);
-	      gsl_vector *Uty=gsl_vector_alloc (y->size);
-
-	      //read relatedness matrix G
-	      if (!(cPar.file_kin).empty()) {
-		cPar.ReadGenotypes (UtX, G, false);
-
-		//read relatedness matrix G
-		ReadFile_kin (cPar.file_kin, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode, cPar.error, G);
-		if (cPar.error==true) {cout<<"error! fail to read kinship/relatedness file. "<<endl; return;}
-
-		//center matrix G
-		CenterMatrix (G);
-	      } else {
-		cPar.ReadGenotypes (UtX, G, true);
-	      }
-
-	      //eigen-decomposition and calculate trace_G
-	      cout<<"Start Eigen-Decomposition..."<<endl;
-	      time_start=clock();
-	      cPar.trace_G=EigenDecomp (G, U, eval, 0);
-	      cPar.trace_G=0.0;
-	      for (size_t i=0; i<eval->size; i++) {
-		if (gsl_vector_get (eval, i)<1e-10) {gsl_vector_set (eval, i, 0);}
-		cPar.trace_G+=gsl_vector_get (eval, i);
-	      }
-	      cPar.trace_G/=(double)eval->size;
-	      cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-
-	      //calculate UtW and Uty
-	      CalcUtX (U, W, UtW);
-	      CalcUtX (U, y, Uty);
-
-	      //calculate REMLE/MLE estimate and pve
-	      CalcLambda ('L', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_mle_null, cPar.logl_mle_H0);
-	      CalcLambda ('R', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region, cPar.l_remle_null, cPar.logl_remle_H0);
-	      CalcPve (eval, UtW, Uty, cPar.l_remle_null, cPar.trace_G, cPar.pve_null, cPar.pve_se_null);
-
-	      cPar.PrintSummary();
-
-	      //Creat and calcualte UtX, use a large memory
-	      cout<<"Calculating UtX..."<<endl;
-	      time_start=clock();
-	      CalcUtX (U, UtX);
-	      cPar.time_UtX=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-
-	      //perform analysis; assume X and y are already centered
-	      BSLMMDAP cBslmmDap;
-	      cBslmmDap.CopyFromParam(cPar);
-	      time_start=clock();
-	      cBslmmDap.DAP_CalcBF (U, UtX, Uty, eval, y);
-	      cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-	      cBslmmDap.CopyToParam(cPar);
-
-	      //release all matrices and vectors
-	      gsl_matrix_free (G);
-	      gsl_matrix_free (U);
-	      gsl_matrix_free (UtW);
-	      gsl_vector_free (eval);
-	      gsl_vector_free (Uty);
-	    }
-
-	    gsl_matrix_free (W);
-	    gsl_vector_free (y);
-	    gsl_matrix_free (UtX);
-	  } else if (cPar.a_mode==15) {
-	    //perform EM algorithm and estimate parameters
-	    vector<string> vec_rs;
-	    vector<double> vec_sa2, vec_sb2, wab;
-	    vector<vector<vector<double> > > BF;
-
-	    //read hyp and bf files (functions defined in BSLMMDAP)
-	    ReadFile_hyb (cPar.file_hyp, vec_sa2, vec_sb2, wab);
-	    ReadFile_bf (cPar.file_bf, vec_rs, BF);
-
-	    cPar.ns_test=vec_rs.size();
-	    if (wab.size()!=BF[0][0].size()) {cout<<"error! hyp and bf files dimension do not match"<<endl;}
-
-	    //load annotations
-	    gsl_matrix *Ac;
-	    gsl_matrix_int *Ad;
-	    gsl_vector_int *dlevel;
-	    size_t kc, kd;
-	    if (!cPar.file_cat.empty()) {
-	      ReadFile_cat (cPar.file_cat, vec_rs, Ac, Ad, dlevel, kc, kd);
-	    } else {
-	      kc=0; kd=0;
-	    }
-
-	    cout<<"## number of blocks = "<<BF.size()<<endl;
-	    cout<<"## number of analyzed SNPs = "<<vec_rs.size()<<endl;
-	    cout<<"## grid size for hyperparameters = "<<wab.size()<<endl;
-	    cout<<"## number of continuous annotations = "<<kc<<endl;
-	    cout<<"## number of discrete annotations = "<<kd<<endl;
-
-	    //DAP_EstimateHyper (const size_t kc, const size_t kd, const vector<string> &vec_rs, const vector<double> &vec_sa2, const vector<double> &vec_sb2, const vector<double> &wab, const vector<vector<vector<double> > > &BF, gsl_matrix *Ac, gsl_matrix_int *Ad, gsl_vector_int *dlevel);
-
-	    //perform analysis
-	    BSLMMDAP cBslmmDap;
-	    cBslmmDap.CopyFromParam(cPar);
-	    time_start=clock();
-	    cBslmmDap.DAP_EstimateHyper (kc, kd, vec_rs, vec_sa2, vec_sb2, wab, BF, Ac, Ad, dlevel);
-	    cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-	    cBslmmDap.CopyToParam(cPar);
-
-	    gsl_matrix_free(Ac);
-	    gsl_matrix_int_free(Ad);
-	    gsl_vector_int_free(dlevel);
-	  } else {
-	    //
-	  }
-
-	}
-
-
-
-
-	/*
-	//LDR (change 14 to 16?)
-	if (cPar.a_mode==14) {
-		gsl_vector *y=gsl_vector_alloc (cPar.ni_test);
-		gsl_matrix *W=gsl_matrix_alloc (y->size, cPar.n_cvt);
-		gsl_matrix *G=gsl_matrix_alloc (1, 1);
-		vector<vector<unsigned char> > Xt;
-
-        	//set covariates matrix W and phenotype vector y
-		//an intercept is included in W
-		cPar.CopyCvtPhen (W, y, 0);
-
-		//read in genotype matrix X
-		cPar.ReadGenotypes (Xt, G, false);
-
-		LDR cLdr;
-		cLdr.CopyFromParam(cPar);
-		time_start=clock();
-
-		cLdr.VB(Xt, W, y);
-
-		cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
-		cLdr.CopyToParam(cPar);
-
-		gsl_vector_free (y);
-		gsl_matrix_free (W);
-		gsl_matrix_free (G);
-	}
-	*/
-
-	cPar.time_total=(clock()-time_begin)/(double(CLOCKS_PER_SEC)*60.0);
-
-	return;
+void GEMMA::BatchRun(PARAM &cPar) {
+  clock_t time_begin, time_start;
+  time_begin = clock();
+
+  // Read Files.
+  cout << "Reading Files ... " << endl;
+  cPar.ReadFiles();
+  if (cPar.error == true) {
+    cout << "error! fail to read files. " << endl;
+    return;
+  }
+  cPar.CheckData();
+  if (cPar.error == true) {
+    cout << "error! fail to check data. " << endl;
+    return;
+  }
+
+  // Prediction for bslmm
+  if (cPar.a_mode == 41 || cPar.a_mode == 42) {
+    gsl_vector *y_prdt;
+
+    y_prdt = gsl_vector_alloc(cPar.ni_total - cPar.ni_test);
+
+    // set to zero
+    gsl_vector_set_zero(y_prdt);
+
+    PRDT cPRDT;
+    cPRDT.CopyFromParam(cPar);
+
+    // add breeding value if needed
+    if (!cPar.file_kin.empty() && !cPar.file_ebv.empty()) {
+      cout << "Adding Breeding Values ... " << endl;
+
+      gsl_matrix *G = gsl_matrix_alloc(cPar.ni_total, cPar.ni_total);
+      gsl_vector *u_hat = gsl_vector_alloc(cPar.ni_test);
+
+      // read kinship matrix and set u_hat
+      vector<int> indicator_all;
+      size_t c_bv = 0;
+      for (size_t i = 0; i < cPar.indicator_idv.size(); i++) {
+        indicator_all.push_back(1);
+        if (cPar.indicator_bv[i] == 1) {
+          gsl_vector_set(u_hat, c_bv, cPar.vec_bv[i]);
+          c_bv++;
+        }
+      }
+
+      ReadFile_kin(cPar.file_kin, indicator_all, cPar.mapID2num, cPar.k_mode,
+                   cPar.error, G);
+      if (cPar.error == true) {
+        cout << "error! fail to read kinship/relatedness file. " << endl;
+        return;
+      }
+
+      // read u
+      cPRDT.AddBV(G, u_hat, y_prdt);
+
+      gsl_matrix_free(G);
+      gsl_vector_free(u_hat);
+    }
+
+    // add beta
+    if (!cPar.file_bfile.empty()) {
+      cPRDT.AnalyzePlink(y_prdt);
+    } else {
+      cPRDT.AnalyzeBimbam(y_prdt);
+    }
+
+    // add mu
+    gsl_vector_add_constant(y_prdt, cPar.pheno_mean);
+
+    // convert y to probability if needed
+    if (cPar.a_mode == 42) {
+      double d;
+      for (size_t i = 0; i < y_prdt->size; i++) {
+        d = gsl_vector_get(y_prdt, i);
+        d = gsl_cdf_gaussian_P(d, 1.0);
+        gsl_vector_set(y_prdt, i, d);
+      }
+    }
+
+    cPRDT.CopyToParam(cPar);
+
+    cPRDT.WriteFiles(y_prdt);
+
+    gsl_vector_free(y_prdt);
+  }
+
+  // Prediction with kinship matrix only; for one or more phenotypes
+  if (cPar.a_mode == 43) {
+    // first, use individuals with full phenotypes to obtain estimates of Vg and
+    // Ve
+    gsl_matrix *Y = gsl_matrix_alloc(cPar.ni_test, cPar.n_ph);
+    gsl_matrix *W = gsl_matrix_alloc(Y->size1, cPar.n_cvt);
+    gsl_matrix *G = gsl_matrix_alloc(Y->size1, Y->size1);
+    gsl_matrix *U = gsl_matrix_alloc(Y->size1, Y->size1);
+    gsl_matrix *UtW = gsl_matrix_alloc(Y->size1, W->size2);
+    gsl_matrix *UtY = gsl_matrix_alloc(Y->size1, Y->size2);
+    gsl_vector *eval = gsl_vector_alloc(Y->size1);
+
+    gsl_matrix *Y_full = gsl_matrix_alloc(cPar.ni_cvt, cPar.n_ph);
+    gsl_matrix *W_full = gsl_matrix_alloc(Y_full->size1, cPar.n_cvt);
+
+    // set covariates matrix W and phenotype matrix Y
+    // an intercept should be included in W,
+    cPar.CopyCvtPhen(W, Y, 0);
+    cPar.CopyCvtPhen(W_full, Y_full, 1);
+
+    gsl_matrix *Y_hat = gsl_matrix_alloc(Y_full->size1, cPar.n_ph);
+    gsl_matrix *G_full = gsl_matrix_alloc(Y_full->size1, Y_full->size1);
+    gsl_matrix *H_full = gsl_matrix_alloc(Y_full->size1 * Y_hat->size2,
+                                          Y_full->size1 * Y_hat->size2);
+
+    // read relatedness matrix G, and matrix G_full
+    ReadFile_kin(cPar.file_kin, cPar.indicator_idv, cPar.mapID2num, cPar.k_mode,
+                 cPar.error, G);
+    if (cPar.error == true) {
+      cout << "error! fail to read kinship/relatedness file. " << endl;
+      return;
+    }
+    ReadFile_kin(cPar.file_kin, cPar.indicator_cvt, cPar.mapID2num, cPar.k_mode,
+                 cPar.error, G_full);
+    if (cPar.error == true) {
+      cout << "error! fail to read kinship/relatedness file. " << endl;
+      return;
+    }
+
+    // center matrix G
+    CenterMatrix(G);
+    CenterMatrix(G_full);
+
+    // eigen-decomposition and calculate trace_G
+    cout << "Start Eigen-Decomposition..." << endl;
+    time_start = clock();
+    cPar.trace_G = EigenDecomp(G, U, eval, 0);
+    cPar.trace_G = 0.0;
+    for (size_t i = 0; i < eval->size; i++) {
+      if (gsl_vector_get(eval, i) < 1e-10) {
+        gsl_vector_set(eval, i, 0);
+      }
+      cPar.trace_G += gsl_vector_get(eval, i);
+    }
+    cPar.trace_G /= (double)eval->size;
+    cPar.time_eigen = (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+
+    // calculate UtW and Uty
+    CalcUtX(U, W, UtW);
+    CalcUtX(U, Y, UtY);
+
+    // calculate variance component and beta estimates
+    // and then obtain predicted values
+    if (cPar.n_ph == 1) {
+      gsl_vector *beta = gsl_vector_alloc(W->size2);
+      gsl_vector *se_beta = gsl_vector_alloc(W->size2);
+
+      double lambda, logl, vg, ve;
+      gsl_vector_view UtY_col = gsl_matrix_column(UtY, 0);
+
+      // obtain estimates
+      CalcLambda('R', eval, UtW, &UtY_col.vector, cPar.l_min, cPar.l_max,
+                 cPar.n_region, lambda, logl);
+      CalcLmmVgVeBeta(eval, UtW, &UtY_col.vector, lambda, vg, ve, beta,
+                      se_beta);
+
+      cout << "REMLE estimate for vg in the null model = " << vg << endl;
+      cout << "REMLE estimate for ve in the null model = " << ve << endl;
+      cPar.vg_remle_null = vg;
+      cPar.ve_remle_null = ve;
+
+      // obtain Y_hat from fixed effects
+      gsl_vector_view Yhat_col = gsl_matrix_column(Y_hat, 0);
+      gsl_blas_dgemv(CblasNoTrans, 1.0, W_full, beta, 0.0, &Yhat_col.vector);
+
+      // obtain H
+      gsl_matrix_set_identity(H_full);
+      gsl_matrix_scale(H_full, ve);
+      gsl_matrix_scale(G_full, vg);
+      gsl_matrix_add(H_full, G_full);
+
+      // free matrices
+      gsl_vector_free(beta);
+      gsl_vector_free(se_beta);
+    } else {
+      gsl_matrix *Vg = gsl_matrix_alloc(cPar.n_ph, cPar.n_ph);
+      gsl_matrix *Ve = gsl_matrix_alloc(cPar.n_ph, cPar.n_ph);
+      gsl_matrix *B = gsl_matrix_alloc(cPar.n_ph, W->size2);
+      gsl_matrix *se_B = gsl_matrix_alloc(cPar.n_ph, W->size2);
+
+      // obtain estimates
+      CalcMvLmmVgVeBeta(eval, UtW, UtY, cPar.em_iter, cPar.nr_iter,
+                        cPar.em_prec, cPar.nr_prec, cPar.l_min, cPar.l_max,
+                        cPar.n_region, Vg, Ve, B, se_B);
+
+      cout << "REMLE estimate for Vg in the null model: " << endl;
+      for (size_t i = 0; i < Vg->size1; i++) {
+        for (size_t j = 0; j <= i; j++) {
+          cout << gsl_matrix_get(Vg, i, j) << "\t";
+        }
+        cout << endl;
+      }
+      cout << "REMLE estimate for Ve in the null model: " << endl;
+      for (size_t i = 0; i < Ve->size1; i++) {
+        for (size_t j = 0; j <= i; j++) {
+          cout << gsl_matrix_get(Ve, i, j) << "\t";
+        }
+        cout << endl;
+      }
+      cPar.Vg_remle_null.clear();
+      cPar.Ve_remle_null.clear();
+      for (size_t i = 0; i < Vg->size1; i++) {
+        for (size_t j = i; j < Vg->size2; j++) {
+          cPar.Vg_remle_null.push_back(gsl_matrix_get(Vg, i, j));
+          cPar.Ve_remle_null.push_back(gsl_matrix_get(Ve, i, j));
+        }
+      }
+
+      // obtain Y_hat from fixed effects
+      gsl_blas_dgemm(CblasNoTrans, CblasTrans, 1.0, W_full, B, 0.0, Y_hat);
+
+      // obtain H
+      KroneckerSym(G_full, Vg, H_full);
+      for (size_t i = 0; i < G_full->size1; i++) {
+        gsl_matrix_view H_sub = gsl_matrix_submatrix(
+            H_full, i * Ve->size1, i * Ve->size2, Ve->size1, Ve->size2);
+        gsl_matrix_add(&H_sub.matrix, Ve);
+      }
+
+      // free matrices
+      gsl_matrix_free(Vg);
+      gsl_matrix_free(Ve);
+      gsl_matrix_free(B);
+      gsl_matrix_free(se_B);
+    }
+
+    PRDT cPRDT;
+
+    cPRDT.CopyFromParam(cPar);
+
+    cout << "Predicting Missing Phentypes ... " << endl;
+    time_start = clock();
+    cPRDT.MvnormPrdt(Y_hat, H_full, Y_full);
+    cPar.time_opt = (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+
+    cPRDT.WriteFiles(Y_full);
+
+    gsl_matrix_free(Y);
+    gsl_matrix_free(W);
+    gsl_matrix_free(G);
+    gsl_matrix_free(U);
+    gsl_matrix_free(UtW);
+    gsl_matrix_free(UtY);
+    gsl_vector_free(eval);
+
+    gsl_matrix_free(Y_full);
+    gsl_matrix_free(Y_hat);
+    gsl_matrix_free(W_full);
+    gsl_matrix_free(G_full);
+    gsl_matrix_free(H_full);
+  }
+
+  // Generate Kinship matrix
+  if (cPar.a_mode == 21 || cPar.a_mode == 22) {
+    cout << "Calculating Relatedness Matrix ... " << endl;
+
+    gsl_matrix *G = gsl_matrix_alloc(cPar.ni_total, cPar.ni_total);
+
+    time_start = clock();
+    cPar.CalcKin(G);
+    cPar.time_G = (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+    if (cPar.error == true) {
+      cout << "error! fail to calculate relatedness matrix. " << endl;
+      return;
+    }
+
+    if (cPar.a_mode == 21) {
+      cPar.WriteMatrix(G, "cXX");
+    } else {
+      cPar.WriteMatrix(G, "sXX");
+    }
+
+    gsl_matrix_free(G);
+  }
+
+  // Compute the LDSC weights (not implemented yet)
+  if (cPar.a_mode == 72) {
+    cout << "Calculating Weights ... " << endl;
+
+    VARCOV cVarcov;
+    cVarcov.CopyFromParam(cPar);
+
+    if (!cPar.file_bfile.empty()) {
+      cVarcov.AnalyzePlink();
+    } else {
+      cVarcov.AnalyzeBimbam();
+    }
+
+    cVarcov.CopyToParam(cPar);
+  }
+
+  // Compute the S matrix (and its variance), that is used for
+  // variance component estimation using summary statistics.
+  if (cPar.a_mode == 25 || cPar.a_mode == 26) {
+    cout << "Calculating the S Matrix ... " << endl;
+
+    gsl_matrix *S = gsl_matrix_alloc(cPar.n_vc * 2, cPar.n_vc);
+    gsl_vector *ns = gsl_vector_alloc(cPar.n_vc + 1);
+    gsl_matrix_set_zero(S);
+    gsl_vector_set_zero(ns);
+
+    gsl_matrix_view S_mat = gsl_matrix_submatrix(S, 0, 0, cPar.n_vc, cPar.n_vc);
+    gsl_matrix_view Svar_mat =
+        gsl_matrix_submatrix(S, cPar.n_vc, 0, cPar.n_vc, cPar.n_vc);
+    gsl_vector_view ns_vec = gsl_vector_subvector(ns, 0, cPar.n_vc);
+
+    gsl_matrix *K = gsl_matrix_alloc(cPar.ni_test, cPar.n_vc * cPar.ni_test);
+    gsl_matrix *A = gsl_matrix_alloc(cPar.ni_test, cPar.n_vc * cPar.ni_test);
+    gsl_matrix_set_zero(K);
+    gsl_matrix_set_zero(A);
+
+    gsl_vector *y = gsl_vector_alloc(cPar.ni_test);
+    gsl_matrix *W = gsl_matrix_alloc(cPar.ni_test, cPar.n_cvt);
+
+    cPar.CopyCvtPhen(W, y, 0);
+
+    set<string> setSnps_beta;
+    map<string, double> mapRS2wA, mapRS2wK;
+
+    cPar.ObtainWeight(setSnps_beta, mapRS2wK);
+
+    time_start = clock();
+    cPar.CalcS(mapRS2wA, mapRS2wK, W, A, K, &S_mat.matrix, &Svar_mat.matrix,
+               &ns_vec.vector);
+    cPar.time_G = (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+    if (cPar.error == true) {
+      cout << "error! fail to calculate the S matrix. " << endl;
+      return;
+    }
+
+    gsl_vector_set(ns, cPar.n_vc, cPar.ni_test);
+
+    cPar.WriteMatrix(S, "S");
+    cPar.WriteVector(ns, "size");
+    cPar.WriteVar("snps");
+
+    gsl_matrix_free(S);
+    gsl_vector_free(ns);
+
+    gsl_matrix_free(A);
+    gsl_matrix_free(K);
+
+    gsl_vector_free(y);
+    gsl_matrix_free(K);
+  }
+
+  // Compute the q vector, that is used for variance component estimation using
+  // summary statistics
+  if (cPar.a_mode == 27 || cPar.a_mode == 28) {
+    gsl_matrix *Vq = gsl_matrix_alloc(cPar.n_vc, cPar.n_vc);
+    gsl_vector *q = gsl_vector_alloc(cPar.n_vc);
+    gsl_vector *s = gsl_vector_alloc(cPar.n_vc + 1);
+    gsl_vector_set_zero(q);
+    gsl_vector_set_zero(s);
+
+    gsl_vector_view s_vec = gsl_vector_subvector(s, 0, cPar.n_vc);
+
+    vector<size_t> vec_cat, vec_ni;
+    vector<double> vec_weight, vec_z2;
+    map<string, double> mapRS2weight;
+    mapRS2weight.clear();
+
+    time_start = clock();
+    ReadFile_beta(cPar.file_beta, cPar.mapRS2cat, mapRS2weight, vec_cat, vec_ni,
+                  vec_weight, vec_z2, cPar.ni_total, cPar.ns_total,
+                  cPar.ns_test);
+    cout << "## number of total individuals = " << cPar.ni_total << endl;
+    cout << "## number of total SNPs = " << cPar.ns_total << endl;
+    cout << "## number of analyzed SNPs = " << cPar.ns_test << endl;
+    cout << "## number of variance components = " << cPar.n_vc << endl;
+    cout << "Calculating the q vector ... " << endl;
+    Calcq(cPar.n_block, vec_cat, vec_ni, vec_weight, vec_z2, Vq, q,
+          &s_vec.vector);
+    cPar.time_G = (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+
+    if (cPar.error == true) {
+      cout << "error! fail to calculate the q vector. " << endl;
+      return;
+    }
+
+    gsl_vector_set(s, cPar.n_vc, cPar.ni_total);
+
+    cPar.WriteMatrix(Vq, "Vq");
+    cPar.WriteVector(q, "q");
+    cPar.WriteVector(s, "size");
+    /*
+    for (size_t i=0; i<cPar.n_vc; i++) {
+      cout<<gsl_vector_get(q, i)<<endl;
+    }
+    */
+    gsl_matrix_free(Vq);
+    gsl_vector_free(q);
+    gsl_vector_free(s);
+  }
+
+  // Calculate SNP covariance.
+  if (cPar.a_mode == 71) {
+    VARCOV cVarcov;
+    cVarcov.CopyFromParam(cPar);
+
+    if (!cPar.file_bfile.empty()) {
+      cVarcov.AnalyzePlink();
+    } else {
+      cVarcov.AnalyzeBimbam();
+    }
+
+    cVarcov.CopyToParam(cPar);
+  }
+
+  // LM.
+  if (cPar.a_mode == 51 || cPar.a_mode == 52 || cPar.a_mode == 53 ||
+      cPar.a_mode == 54) { // Fit LM
+    gsl_matrix *Y = gsl_matrix_alloc(cPar.ni_test, cPar.n_ph);
+    gsl_matrix *W = gsl_matrix_alloc(Y->size1, cPar.n_cvt);
+
+    // set covariates matrix W and phenotype matrix Y
+    // an intercept should be included in W,
+    cPar.CopyCvtPhen(W, Y, 0);
+
+    // Fit LM or mvLM
+    if (cPar.n_ph == 1) {
+      LM cLm;
+      cLm.CopyFromParam(cPar);
+
+      gsl_vector_view Y_col = gsl_matrix_column(Y, 0);
+
+      if (!cPar.file_gene.empty()) {
+        cLm.AnalyzeGene(W,
+                        &Y_col.vector); // y is the predictor, not the phenotype
+      } else if (!cPar.file_bfile.empty()) {
+        cLm.AnalyzePlink(W, &Y_col.vector);
+      } else if (!cPar.file_oxford.empty()) {
+        cLm.Analyzebgen(W, &Y_col.vector);
+      } else {
+        cLm.AnalyzeBimbam(W, &Y_col.vector);
+      }
+
+      cLm.WriteFiles();
+      cLm.CopyToParam(cPar);
+    }
+    /*
+    else {
+            MVLM cMvlm;
+            cMvlm.CopyFromParam(cPar);
+
+            if (!cPar.file_bfile.empty()) {
+                    cMvlm.AnalyzePlink (W, Y);
+            } else {
+                    cMvlm.AnalyzeBimbam (W, Y);
+            }
+
+            cMvlm.WriteFiles();
+            cMvlm.CopyToParam(cPar);
+    }
+    */
+    // release all matrices and vectors
+    gsl_matrix_free(Y);
+    gsl_matrix_free(W);
+  }
+
+  // VC estimation with one or multiple kinship matrices
+  // REML approach only
+  // if file_kin or file_ku/kd is provided, then a_mode is changed to 5 already,
+  // in param.cpp
+  // for one phenotype only;
+  if (cPar.a_mode == 61 || cPar.a_mode == 62 || cPar.a_mode == 63) {
+    if (!cPar.file_beta.empty()) {
+      // need to obtain a common set of SNPs between beta file and the genotype
+      // file; these are saved in mapRS2wA and mapRS2wK
+      // normalize the weight in mapRS2wK to have an average of one; each
+      // element of mapRS2wA is 1
+      // update indicator_snps, so that the numbers are in accordance with
+      // mapRS2wK
+      set<string> setSnps_beta;
+      ReadFile_snps_header(cPar.file_beta, setSnps_beta);
+
+      map<string, double> mapRS2wA, mapRS2wK;
+      cPar.ObtainWeight(setSnps_beta, mapRS2wK);
+
+      cPar.UpdateSNP(mapRS2wK);
+
+      // Setup matrices and vectors.
+      gsl_matrix *S = gsl_matrix_alloc(cPar.n_vc * 2, cPar.n_vc);
+      gsl_matrix *Vq = gsl_matrix_alloc(cPar.n_vc, cPar.n_vc);
+      gsl_vector *q = gsl_vector_alloc(cPar.n_vc);
+      gsl_vector *s = gsl_vector_alloc(cPar.n_vc + 1);
+
+      gsl_matrix *K = gsl_matrix_alloc(cPar.ni_test, cPar.n_vc * cPar.ni_test);
+      gsl_matrix *A = gsl_matrix_alloc(cPar.ni_test, cPar.n_vc * cPar.ni_test);
+
+      gsl_vector *y = gsl_vector_alloc(cPar.ni_test);
+      gsl_matrix *W = gsl_matrix_alloc(cPar.ni_test, cPar.n_cvt);
+
+      gsl_matrix_set_zero(K);
+      gsl_matrix_set_zero(A);
+
+      gsl_matrix_set_zero(S);
+      gsl_matrix_set_zero(Vq);
+      gsl_vector_set_zero(q);
+      gsl_vector_set_zero(s);
+
+      cPar.CopyCvtPhen(W, y, 0);
+
+      gsl_matrix_view S_mat =
+          gsl_matrix_submatrix(S, 0, 0, cPar.n_vc, cPar.n_vc);
+      gsl_matrix_view Svar_mat =
+          gsl_matrix_submatrix(S, cPar.n_vc, 0, cPar.n_vc, cPar.n_vc);
+      gsl_vector_view s_vec = gsl_vector_subvector(s, 0, cPar.n_vc);
+
+      vector<size_t> vec_cat, vec_ni;
+      vector<double> vec_weight, vec_z2;
+
+      // read beta, based on the mapRS2wK
+      ReadFile_beta(cPar.file_beta, cPar.mapRS2cat, mapRS2wK, vec_cat, vec_ni,
+                    vec_weight, vec_z2, cPar.ni_study, cPar.ns_study,
+                    cPar.ns_test);
+
+      cout << "Study Panel: " << endl;
+      cout << "## number of total individuals = " << cPar.ni_study << endl;
+      cout << "## number of total SNPs = " << cPar.ns_study << endl;
+      cout << "## number of analyzed SNPs = " << cPar.ns_test << endl;
+      cout << "## number of variance components = " << cPar.n_vc << endl;
+
+      // compute q
+      Calcq(cPar.n_block, vec_cat, vec_ni, vec_weight, vec_z2, Vq, q,
+            &s_vec.vector);
+
+      // compute S
+      time_start = clock();
+      cPar.CalcS(mapRS2wA, mapRS2wK, W, A, K, &S_mat.matrix, &Svar_mat.matrix,
+                 &s_vec.vector);
+      cPar.time_G += (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+      if (cPar.error == true) {
+        cout << "error! fail to calculate the S matrix. " << endl;
+        return;
+      }
+
+      // compute vc estimates
+      CalcVCss(Vq, &S_mat.matrix, &Svar_mat.matrix, q, &s_vec.vector,
+               cPar.ni_study, cPar.v_pve, cPar.v_se_pve, cPar.pve_total,
+               cPar.se_pve_total, cPar.v_sigma2, cPar.v_se_sigma2,
+               cPar.v_enrich, cPar.v_se_enrich);
+
+      // if LDSC weights, then compute the weights and run the above steps again
+      if (cPar.a_mode == 62) {
+        // compute the weights and normalize the weights for A
+        cPar.UpdateWeight(1, mapRS2wK, cPar.ni_study, &s_vec.vector, mapRS2wA);
+
+        // read beta file again, and update weigths vector
+        ReadFile_beta(cPar.file_beta, cPar.mapRS2cat, mapRS2wA, vec_cat, vec_ni,
+                      vec_weight, vec_z2, cPar.ni_study, cPar.ns_total,
+                      cPar.ns_test);
+
+        // compute q
+        Calcq(cPar.n_block, vec_cat, vec_ni, vec_weight, vec_z2, Vq, q,
+              &s_vec.vector);
+
+        // compute S
+        time_start = clock();
+        cPar.CalcS(mapRS2wA, mapRS2wK, W, A, K, &S_mat.matrix, &Svar_mat.matrix,
+                   &s_vec.vector);
+        cPar.time_G += (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+        if (cPar.error == true) {
+          cout << "error! fail to calculate the S matrix. " << endl;
+          return;
+        }
+
+        // compute vc estimates
+        CalcVCss(Vq, &S_mat.matrix, &Svar_mat.matrix, q, &s_vec.vector,
+                 cPar.ni_study, cPar.v_pve, cPar.v_se_pve, cPar.pve_total,
+                 cPar.se_pve_total, cPar.v_sigma2, cPar.v_se_sigma2,
+                 cPar.v_enrich, cPar.v_se_enrich);
+      }
+
+      gsl_vector_set(s, cPar.n_vc, cPar.ni_test);
+
+      cPar.WriteMatrix(S, "S");
+      cPar.WriteMatrix(Vq, "Vq");
+      cPar.WriteVector(q, "q");
+      cPar.WriteVector(s, "size");
+
+      gsl_matrix_free(S);
+      gsl_matrix_free(Vq);
+      gsl_vector_free(q);
+      gsl_vector_free(s);
+
+      gsl_matrix_free(A);
+      gsl_matrix_free(K);
+      gsl_vector_free(y);
+      gsl_matrix_free(W);
+    } else if (!cPar.file_study.empty() || !cPar.file_mstudy.empty()) {
+      if (!cPar.file_study.empty()) {
+        string sfile = cPar.file_study + ".size.txt";
+        CountFileLines(sfile, cPar.n_vc);
+      } else {
+        string file_name;
+        igzstream infile(cPar.file_mstudy.c_str(), igzstream::in);
+        if (!infile) {
+          cout << "error! fail to open mstudy file: " << cPar.file_study
+               << endl;
+          return;
+        }
+
+        safeGetline(infile, file_name);
+
+        infile.clear();
+        infile.close();
+
+        string sfile = file_name + ".size.txt";
+        CountFileLines(sfile, cPar.n_vc);
+      }
+
+      cPar.n_vc = cPar.n_vc - 1;
+
+      gsl_matrix *S = gsl_matrix_alloc(2 * cPar.n_vc, cPar.n_vc);
+      gsl_matrix *Vq = gsl_matrix_alloc(cPar.n_vc, cPar.n_vc);
+      // gsl_matrix *V=gsl_matrix_alloc (cPar.n_vc+1,
+      // (cPar.n_vc*(cPar.n_vc+1))/2*(cPar.n_vc+1) );
+      // gsl_matrix *Vslope=gsl_matrix_alloc (n_lines+1,
+      // (n_lines*(n_lines+1))/2*(n_lines+1) );
+      gsl_vector *q = gsl_vector_alloc(cPar.n_vc);
+      gsl_vector *s_study = gsl_vector_alloc(cPar.n_vc);
+      gsl_vector *s_ref = gsl_vector_alloc(cPar.n_vc);
+      gsl_vector *s = gsl_vector_alloc(cPar.n_vc + 1);
+
+      gsl_matrix_set_zero(S);
+      gsl_matrix_view S_mat =
+          gsl_matrix_submatrix(S, 0, 0, cPar.n_vc, cPar.n_vc);
+      gsl_matrix_view Svar_mat =
+          gsl_matrix_submatrix(S, cPar.n_vc, 0, cPar.n_vc, cPar.n_vc);
+
+      gsl_matrix_set_zero(Vq);
+      // gsl_matrix_set_zero(V);
+      // gsl_matrix_set_zero(Vslope);
+      gsl_vector_set_zero(q);
+      gsl_vector_set_zero(s_study);
+      gsl_vector_set_zero(s_ref);
+
+      if (!cPar.file_study.empty()) {
+        ReadFile_study(cPar.file_study, Vq, q, s_study, cPar.ni_study);
+      } else {
+        ReadFile_mstudy(cPar.file_mstudy, Vq, q, s_study, cPar.ni_study);
+      }
+
+      if (!cPar.file_ref.empty()) {
+        ReadFile_ref(cPar.file_ref, &S_mat.matrix, &Svar_mat.matrix, s_ref,
+                     cPar.ni_ref);
+      } else {
+        ReadFile_mref(cPar.file_mref, &S_mat.matrix, &Svar_mat.matrix, s_ref,
+                      cPar.ni_ref);
+      }
+
+      cout << "## number of variance components = " << cPar.n_vc << endl;
+      cout << "## number of individuals in the sample = " << cPar.ni_study
+           << endl;
+      cout << "## number of individuals in the reference = " << cPar.ni_ref
+           << endl;
+
+      CalcVCss(Vq, &S_mat.matrix, &Svar_mat.matrix, q, s_study, cPar.ni_study,
+               cPar.v_pve, cPar.v_se_pve, cPar.pve_total, cPar.se_pve_total,
+               cPar.v_sigma2, cPar.v_se_sigma2, cPar.v_enrich,
+               cPar.v_se_enrich);
+
+      gsl_vector_view s_sub = gsl_vector_subvector(s, 0, cPar.n_vc);
+      gsl_vector_memcpy(&s_sub.vector, s_ref);
+      gsl_vector_set(s, cPar.n_vc, cPar.ni_ref);
+
+      cPar.WriteMatrix(S, "S");
+      cPar.WriteMatrix(Vq, "Vq");
+      cPar.WriteVector(q, "q");
+      cPar.WriteVector(s, "size");
+
+      gsl_matrix_free(S);
+      gsl_matrix_free(Vq);
+      // gsl_matrix_free (V);
+      // gsl_matrix_free (Vslope);
+      gsl_vector_free(q);
+      gsl_vector_free(s_study);
+      gsl_vector_free(s_ref);
+      gsl_vector_free(s);
+    } else {
+      gsl_matrix *Y = gsl_matrix_alloc(cPar.ni_test, cPar.n_ph);
+      gsl_matrix *W = gsl_matrix_alloc(Y->size1, cPar.n_cvt);
+      gsl_matrix *G = gsl_matrix_alloc(Y->size1, Y->size1 * cPar.n_vc);
+
+      // set covariates matrix W and phenotype matrix Y
+      // an intercept should be included in W,
+      cPar.CopyCvtPhen(W, Y, 0);
+
+      // read kinship matrices
+      if (!(cPar.file_mk).empty()) {
+        ReadFile_mk(cPar.file_mk, cPar.indicator_idv, cPar.mapID2num,
+                    cPar.k_mode, cPar.error, G);
+        if (cPar.error == true) {
+          cout << "error! fail to read kinship/relatedness file. " << endl;
+          return;
+        }
+
+        // center matrix G, and obtain v_traceG
+        double d = 0;
+        (cPar.v_traceG).clear();
+        for (size_t i = 0; i < cPar.n_vc; i++) {
+          gsl_matrix_view G_sub =
+              gsl_matrix_submatrix(G, 0, i * G->size1, G->size1, G->size1);
+          CenterMatrix(&G_sub.matrix);
+          d = 0;
+          for (size_t j = 0; j < G->size1; j++) {
+            d += gsl_matrix_get(&G_sub.matrix, j, j);
+          }
+          d /= (double)G->size1;
+          (cPar.v_traceG).push_back(d);
+        }
+      } else if (!(cPar.file_kin).empty()) {
+        ReadFile_kin(cPar.file_kin, cPar.indicator_idv, cPar.mapID2num,
+                     cPar.k_mode, cPar.error, G);
+        if (cPar.error == true) {
+          cout << "error! fail to read kinship/relatedness file. " << endl;
+          return;
+        }
+
+        // center matrix G
+        CenterMatrix(G);
+
+        (cPar.v_traceG).clear();
+        double d = 0;
+        for (size_t j = 0; j < G->size1; j++) {
+          d += gsl_matrix_get(G, j, j);
+        }
+        d /= (double)G->size1;
+        (cPar.v_traceG).push_back(d);
+      }
+      /*
+      //eigen-decomposition and calculate trace_G
+      cout<<"Start Eigen-Decomposition..."<<endl;
+      time_start=clock();
+
+      if (cPar.a_mode==31) {
+              cPar.trace_G=EigenDecomp (G, U, eval, 1);
+      } else {
+              cPar.trace_G=EigenDecomp (G, U, eval, 0);
+      }
+
+      cPar.trace_G=0.0;
+      for (size_t i=0; i<eval->size; i++) {
+              if (gsl_vector_get (eval, i)<1e-10) {gsl_vector_set (eval, i, 0);}
+              cPar.trace_G+=gsl_vector_get (eval, i);
+      }
+      cPar.trace_G/=(double)eval->size;
+
+      cPar.time_eigen=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
+} else {
+      ReadFile_eigenU (cPar.file_ku, cPar.error, U);
+      if (cPar.error==true) {cout<<"error! fail to read the U file. "<<endl;
+return;}
+
+      ReadFile_eigenD (cPar.file_kd, cPar.error, eval);
+      if (cPar.error==true) {cout<<"error! fail to read the D file. "<<endl;
+return;}
+
+      cPar.trace_G=0.0;
+      for (size_t i=0; i<eval->size; i++) {
+              if (gsl_vector_get(eval, i)<1e-10) {gsl_vector_set(eval, i, 0);}
+              cPar.trace_G+=gsl_vector_get(eval, i);
+      }
+      cPar.trace_G/=(double)eval->size;
 }
+*/
+      // fit multiple variance components
+      if (cPar.n_ph == 1) {
+        //		  if (cPar.n_vc==1) {
+        /*
+        //calculate UtW and Uty
+        CalcUtX (U, W, UtW);
+        CalcUtX (U, Y, UtY);
+
+        gsl_vector_view beta=gsl_matrix_row (B, 0);
+        gsl_vector_view se_beta=gsl_matrix_row (se_B, 0);
+        gsl_vector_view UtY_col=gsl_matrix_column (UtY, 0);
+
+        CalcLambda ('L', eval, UtW, &UtY_col.vector, cPar.l_min, cPar.l_max,
+        cPar.n_region, cPar.l_mle_null, cPar.logl_mle_H0);
+        CalcLmmVgVeBeta (eval, UtW, &UtY_col.vector, cPar.l_mle_null,
+        cPar.vg_mle_null, cPar.ve_mle_null, &beta.vector, &se_beta.vector);
+
+        cPar.beta_mle_null.clear();
+        cPar.se_beta_mle_null.clear();
+        for (size_t i=0; i<B->size2; i++) {
+          cPar.beta_mle_null.push_back(gsl_matrix_get(B, 0, i) );
+          cPar.se_beta_mle_null.push_back(gsl_matrix_get(se_B, 0, i) );
+        }
+
+        CalcLambda ('R', eval, UtW, &UtY_col.vector, cPar.l_min, cPar.l_max,
+        cPar.n_region, cPar.l_remle_null, cPar.logl_remle_H0);
+        CalcLmmVgVeBeta (eval, UtW, &UtY_col.vector, cPar.l_remle_null,
+        cPar.vg_remle_null, cPar.ve_remle_null, &beta.vector, &se_beta.vector);
+        cPar.beta_remle_null.clear();
+        cPar.se_beta_remle_null.clear();
+        for (size_t i=0; i<B->size2; i++) {
+          cPar.beta_remle_null.push_back(gsl_matrix_get(B, 0, i) );
+          cPar.se_beta_remle_null.push_back(gsl_matrix_get(se_B, 0, i) );
+        }
+
+        CalcPve (eval, UtW, &UtY_col.vector, cPar.l_remle_null, cPar.trace_G,
+        cPar.pve_null, cPar.pve_se_null);
+        cPar.PrintSummary();
+
+        //calculate and output residuals
+        if (cPar.a_mode==5) {
+          gsl_vector *Utu_hat=gsl_vector_alloc (Y->size1);
+          gsl_vector *Ute_hat=gsl_vector_alloc (Y->size1);
+          gsl_vector *u_hat=gsl_vector_alloc (Y->size1);
+          gsl_vector *e_hat=gsl_vector_alloc (Y->size1);
+          gsl_vector *y_hat=gsl_vector_alloc (Y->size1);
+
+          //obtain Utu and Ute
+          gsl_vector_memcpy (y_hat, &UtY_col.vector);
+          gsl_blas_dgemv (CblasNoTrans, -1.0, UtW, &beta.vector, 1.0, y_hat);
+
+          double d, u, e;
+          for (size_t i=0; i<eval->size; i++) {
+            d=gsl_vector_get (eval, i);
+            u=cPar.l_remle_null*d/(cPar.l_remle_null*d+1.0)*gsl_vector_get(y_hat,
+        i);
+            e=1.0/(cPar.l_remle_null*d+1.0)*gsl_vector_get(y_hat, i);
+            gsl_vector_set (Utu_hat, i, u);
+            gsl_vector_set (Ute_hat, i, e);
+          }
+
+          //obtain u and e
+          gsl_blas_dgemv (CblasNoTrans, 1.0, U, Utu_hat, 0.0, u_hat);
+          gsl_blas_dgemv (CblasNoTrans, 1.0, U, Ute_hat, 0.0, e_hat);
+
+          //output residuals
+          cPar.WriteVector(u_hat, "residU");
+          cPar.WriteVector(e_hat, "residE");
+
+          gsl_vector_free(u_hat);
+          gsl_vector_free(e_hat);
+          gsl_vector_free(y_hat);
+        }
+*/
+        //		  } else {
+        gsl_vector_view Y_col = gsl_matrix_column(Y, 0);
+        VC cVc;
+        cVc.CopyFromParam(cPar);
+        if (cPar.a_mode == 61) {
+          cVc.CalcVChe(G, W, &Y_col.vector);
+        } else if (cPar.a_mode == 62) {
+          cVc.CalcVCreml(cPar.noconstrain, G, W, &Y_col.vector);
+        } else {
+          cVc.CalcVCacl(G, W, &Y_col.vector);
+        }
+        cVc.CopyToParam(cPar);
+        // obtain pve from sigma2
+        // obtain se_pve from se_sigma2
+
+        //}
+      }
+    }
+  }
+
+  // compute confidence intervals with additional summary statistics
+  // we do not check the sign of z-scores here, but they have to be matched with
+  // the genotypes
+  if (cPar.a_mode == 66 || cPar.a_mode == 67) {
+    // read reference file first
+    gsl_matrix *S = gsl_matrix_alloc(cPar.n_vc, cPar.n_vc);
+    gsl_matrix *Svar = gsl_matrix_alloc(cPar.n_vc, cPar.n_vc);
+    gsl_vector *s_ref = gsl_vector_alloc(cPar.n_vc);
+
+    gsl_matrix_set_zero(S);
+    gsl_matrix_set_zero(Svar);
+    gsl_vector_set_zero(s_ref);
+
+    if (!cPar.file_ref.empty()) {
+      ReadFile_ref(cPar.file_ref, S, Svar, s_ref, cPar.ni_ref);
+    } else {
+      ReadFile_mref(cPar.file_mref, S, Svar, s_ref, cPar.ni_ref);
+    }
+
+    // need to obtain a common set of SNPs between beta file and the genotype
+    // file; these are saved in mapRS2wA and mapRS2wK
+    // normalize the weight in mapRS2wK to have an average of one; each element
+    // of mapRS2wA is 1
+    set<string> setSnps_beta;
+    ReadFile_snps_header(cPar.file_beta, setSnps_beta);
+
+    // obtain the weights for wA, which contains the SNP weights for SNPs used
+    // in the model
+    map<string, double> mapRS2wK;
+    cPar.ObtainWeight(setSnps_beta, mapRS2wK);
+
+    // set up matrices and vector
+    gsl_matrix *Xz = gsl_matrix_alloc(cPar.ni_test, cPar.n_vc);
+    gsl_matrix *XWz = gsl_matrix_alloc(cPar.ni_test, cPar.n_vc);
+    gsl_matrix *XtXWz =
+        gsl_matrix_alloc(mapRS2wK.size(), cPar.n_vc * cPar.n_vc);
+    gsl_vector *w = gsl_vector_alloc(mapRS2wK.size());
+    gsl_vector *w1 = gsl_vector_alloc(mapRS2wK.size());
+    gsl_vector *z = gsl_vector_alloc(mapRS2wK.size());
+    gsl_vector *s_vec = gsl_vector_alloc(cPar.n_vc);
+
+    vector<size_t> vec_cat, vec_size;
+    vector<double> vec_z;
+
+    map<string, double> mapRS2z, mapRS2wA;
+    map<string, string> mapRS2A1;
+    string file_str;
+
+    // update s_vec, the number of snps in each category
+    for (size_t i = 0; i < cPar.n_vc; i++) {
+      vec_size.push_back(0);
+    }
+
+    for (map<string, double>::const_iterator it = mapRS2wK.begin();
+         it != mapRS2wK.end(); ++it) {
+      vec_size[cPar.mapRS2cat[it->first]]++;
+    }
+
+    for (size_t i = 0; i < cPar.n_vc; i++) {
+      gsl_vector_set(s_vec, i, vec_size[i]);
+    }
+
+    // update mapRS2wA using v_pve and s_vec
+    if (cPar.a_mode == 66) {
+      for (map<string, double>::const_iterator it = mapRS2wK.begin();
+           it != mapRS2wK.end(); ++it) {
+        mapRS2wA[it->first] = 1;
+      }
+    } else {
+      cPar.UpdateWeight(0, mapRS2wK, cPar.ni_test, s_vec, mapRS2wA);
+    }
+
+    // read in z-scores based on allele 0, and save that into a vector
+    ReadFile_beta(cPar.file_beta, mapRS2wA, mapRS2A1, mapRS2z);
+
+    // update snp indicator, save weights to w, save z-scores to vec_z, save
+    // category label to vec_cat
+    // sign of z is determined by matching alleles
+    cPar.UpdateSNPnZ(mapRS2wA, mapRS2A1, mapRS2z, w, z, vec_cat);
+
+    // compute an n by k matrix of X_iWz
+    cout << "Calculating Xz ... " << endl;
+
+    gsl_matrix_set_zero(Xz);
+    gsl_vector_set_all(w1, 1);
+
+    if (!cPar.file_bfile.empty()) {
+      file_str = cPar.file_bfile + ".bed";
+      PlinkXwz(file_str, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp,
+               vec_cat, w1, z, 0, Xz);
+    } else if (!cPar.file_geno.empty()) {
+      BimbamXwz(cPar.file_geno, cPar.d_pace, cPar.indicator_idv,
+                cPar.indicator_snp, vec_cat, w1, z, 0, Xz);
+    } else if (!cPar.file_mbfile.empty()) {
+      MFILEXwz(1, cPar.file_mbfile, cPar.d_pace, cPar.indicator_idv,
+               cPar.mindicator_snp, vec_cat, w1, z, Xz);
+    } else if (!cPar.file_mgeno.empty()) {
+      MFILEXwz(0, cPar.file_mgeno, cPar.d_pace, cPar.indicator_idv,
+               cPar.mindicator_snp, vec_cat, w1, z, Xz);
+    }
+    /*
+    cout<<"Xz: "<<endl;
+    for (size_t i=0; i<5; i++) {
+      for (size_t j=0; j<cPar.n_vc; j++) {
+        cout<<gsl_matrix_get (Xz, i, j)<<" ";
+      }
+      cout<<endl;
+    }
+    */
+    if (cPar.a_mode == 66) {
+      gsl_matrix_memcpy(XWz, Xz);
+    } else if (cPar.a_mode == 67) {
+      cout << "Calculating XWz ... " << endl;
+
+      gsl_matrix_set_zero(XWz);
+
+      if (!cPar.file_bfile.empty()) {
+        file_str = cPar.file_bfile + ".bed";
+        PlinkXwz(file_str, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp,
+                 vec_cat, w, z, 0, XWz);
+      } else if (!cPar.file_geno.empty()) {
+        BimbamXwz(cPar.file_geno, cPar.d_pace, cPar.indicator_idv,
+                  cPar.indicator_snp, vec_cat, w, z, 0, XWz);
+      } else if (!cPar.file_mbfile.empty()) {
+        MFILEXwz(1, cPar.file_mbfile, cPar.d_pace, cPar.indicator_idv,
+                 cPar.mindicator_snp, vec_cat, w, z, XWz);
+      } else if (!cPar.file_mgeno.empty()) {
+        MFILEXwz(0, cPar.file_mgeno, cPar.d_pace, cPar.indicator_idv,
+                 cPar.mindicator_snp, vec_cat, w, z, XWz);
+      }
+    }
+    /*
+    cout<<"XWz: "<<endl;
+    for (size_t i=0; i<5; i++) {
+      cout<<gsl_vector_get (w, i)<<endl;
+      for (size_t j=0; j<cPar.n_vc; j++) {
+        cout<<gsl_matrix_get (XWz, i, j)<<" ";
+      }
+      cout<<endl;
+    }
+    */
+    // compute an p by k matrix of X_j^TWX_iWz
+    cout << "Calculating XtXWz ... " << endl;
+    gsl_matrix_set_zero(XtXWz);
+
+    if (!cPar.file_bfile.empty()) {
+      file_str = cPar.file_bfile + ".bed";
+      PlinkXtXwz(file_str, cPar.d_pace, cPar.indicator_idv, cPar.indicator_snp,
+                 XWz, 0, XtXWz);
+    } else if (!cPar.file_geno.empty()) {
+      BimbamXtXwz(cPar.file_geno, cPar.d_pace, cPar.indicator_idv,
+                  cPar.indicator_snp, XWz, 0, XtXWz);
+    } else if (!cPar.file_mbfile.empty()) {
+      MFILEXtXwz(1, cPar.file_mbfile, cPar.d_pace, cPar.indicator_idv,
+                 cPar.mindicator_snp, XWz, XtXWz);
+    } else if (!cPar.file_mgeno.empty()) {
+      MFILEXtXwz(0, cPar.file_mgeno, cPar.d_pace, cPar.indicator_idv,
+                 cPar.mindicator_snp, XWz, XtXWz);
+    }
+    /*
+    cout<<"XtXWz: "<<endl;
+    for (size_t i=0; i<5; i++) {
+      for (size_t j=0; j<cPar.n_vc; j++) {
+        cout<<gsl_matrix_get (XtXWz, i, j)<<" ";
+      }
+      cout<<endl;
+    }
+    */
+    // compute confidence intervals
+    CalcCIss(Xz, XWz, XtXWz, S, Svar, w, z, s_vec, vec_cat, cPar.v_pve,
+             cPar.v_se_pve, cPar.pve_total, cPar.se_pve_total, cPar.v_sigma2,
+             cPar.v_se_sigma2, cPar.v_enrich, cPar.v_se_enrich);
+
+    // write files
+    // cPar.WriteMatrix (XWz, "XWz");
+    // cPar.WriteMatrix (XtXWz, "XtXWz");
+    // cPar.WriteVector (w, "w");
+
+    gsl_matrix_free(S);
+    gsl_matrix_free(Svar);
+    gsl_vector_free(s_ref);
+
+    gsl_matrix_free(Xz);
+    gsl_matrix_free(XWz);
+    gsl_matrix_free(XtXWz);
+    gsl_vector_free(w);
+    gsl_vector_free(w1);
+    gsl_vector_free(z);
+    gsl_vector_free(s_vec);
+  }
+
+  // LMM or mvLMM or Eigen-Decomposition
+  if (cPar.a_mode == 1 || cPar.a_mode == 2 || cPar.a_mode == 3 ||
+      cPar.a_mode == 4 || cPar.a_mode == 5 ||
+      cPar.a_mode == 31) { // Fit LMM or mvLMM or eigen
+    gsl_matrix *Y = gsl_matrix_alloc(cPar.ni_test, cPar.n_ph);
+    gsl_matrix *W = gsl_matrix_alloc(Y->size1, cPar.n_cvt);
+    gsl_matrix *B = gsl_matrix_alloc(Y->size2, W->size2); // B is a d by c
+                                                          // matrix
+    gsl_matrix *se_B = gsl_matrix_alloc(Y->size2, W->size2);
+    gsl_matrix *G = gsl_matrix_alloc(Y->size1, Y->size1);
+    gsl_matrix *U = gsl_matrix_alloc(Y->size1, Y->size1);
+    gsl_matrix *UtW = gsl_matrix_alloc(Y->size1, W->size2);
+    gsl_matrix *UtY = gsl_matrix_alloc(Y->size1, Y->size2);
+    gsl_vector *eval = gsl_vector_alloc(Y->size1);
+    gsl_vector *env = gsl_vector_alloc(Y->size1);
+    gsl_vector *weight = gsl_vector_alloc(Y->size1);
+
+    // set covariates matrix W and phenotype matrix Y
+    // an intercept should be included in W,
+    cPar.CopyCvtPhen(W, Y, 0);
+    if (!cPar.file_gxe.empty()) {
+      cPar.CopyGxe(env);
+    }
+
+    // read relatedness matrix G
+    if (!(cPar.file_kin).empty()) {
+      ReadFile_kin(cPar.file_kin, cPar.indicator_idv, cPar.mapID2num,
+                   cPar.k_mode, cPar.error, G);
+      if (cPar.error == true) {
+        cout << "error! fail to read kinship/relatedness file. " << endl;
+        return;
+      }
+
+      // center matrix G
+      CenterMatrix(G);
+
+      // is residual weights are provided, then
+      if (!cPar.file_weight.empty()) {
+        cPar.CopyWeight(weight);
+        double d, wi, wj;
+        for (size_t i = 0; i < G->size1; i++) {
+          wi = gsl_vector_get(weight, i);
+          for (size_t j = i; j < G->size2; j++) {
+            wj = gsl_vector_get(weight, j);
+            d = gsl_matrix_get(G, i, j);
+            if (wi <= 0 || wj <= 0) {
+              d = 0;
+            } else {
+              d /= sqrt(wi * wj);
+            }
+            gsl_matrix_set(G, i, j, d);
+            if (j != i) {
+              gsl_matrix_set(G, j, i, d);
+            }
+          }
+        }
+      }
+
+      // eigen-decomposition and calculate trace_G
+      cout << "Start Eigen-Decomposition..." << endl;
+      time_start = clock();
+
+      if (cPar.a_mode == 31) {
+        cPar.trace_G = EigenDecomp(G, U, eval, 1);
+      } else {
+        cPar.trace_G = EigenDecomp(G, U, eval, 0);
+      }
+
+      if (!cPar.file_weight.empty()) {
+        double wi;
+        for (size_t i = 0; i < U->size1; i++) {
+          wi = gsl_vector_get(weight, i);
+          if (wi <= 0) {
+            wi = 0;
+          } else {
+            wi = sqrt(wi);
+          }
+          gsl_vector_view Urow = gsl_matrix_row(U, i);
+          gsl_vector_scale(&Urow.vector, wi);
+        }
+      }
+
+      cPar.trace_G = 0.0;
+      for (size_t i = 0; i < eval->size; i++) {
+        if (gsl_vector_get(eval, i) < 1e-10) {
+          gsl_vector_set(eval, i, 0);
+        }
+        cPar.trace_G += gsl_vector_get(eval, i);
+      }
+      cPar.trace_G /= (double)eval->size;
+
+      cPar.time_eigen =
+          (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+    } else {
+      ReadFile_eigenU(cPar.file_ku, cPar.error, U);
+      if (cPar.error == true) {
+        cout << "error! fail to read the U file. " << endl;
+        return;
+      }
+
+      ReadFile_eigenD(cPar.file_kd, cPar.error, eval);
+      if (cPar.error == true) {
+        cout << "error! fail to read the D file. " << endl;
+        return;
+      }
+
+      cPar.trace_G = 0.0;
+      for (size_t i = 0; i < eval->size; i++) {
+        if (gsl_vector_get(eval, i) < 1e-10) {
+          gsl_vector_set(eval, i, 0);
+        }
+        cPar.trace_G += gsl_vector_get(eval, i);
+      }
+      cPar.trace_G /= (double)eval->size;
+    }
+
+    if (cPar.a_mode == 31) {
+      cPar.WriteMatrix(U, "eigenU");
+      cPar.WriteVector(eval, "eigenD");
+    } else if (!cPar.file_gene.empty()) {
+      // calculate UtW and Uty
+      CalcUtX(U, W, UtW);
+      CalcUtX(U, Y, UtY);
+
+      LMM cLmm;
+      cLmm.CopyFromParam(cPar);
+
+      gsl_vector_view Y_col = gsl_matrix_column(Y, 0);
+      gsl_vector_view UtY_col = gsl_matrix_column(UtY, 0);
+
+      cLmm.AnalyzeGene(U, eval, UtW, &UtY_col.vector, W,
+                       &Y_col.vector); // y is the predictor, not the phenotype
+
+      cLmm.WriteFiles();
+      cLmm.CopyToParam(cPar);
+    } else {
+      // calculate UtW and Uty
+      CalcUtX(U, W, UtW);
+      CalcUtX(U, Y, UtY);
+
+      // calculate REMLE/MLE estimate and pve for univariate model
+      if (cPar.n_ph == 1) {
+        gsl_vector_view beta = gsl_matrix_row(B, 0);
+        gsl_vector_view se_beta = gsl_matrix_row(se_B, 0);
+        gsl_vector_view UtY_col = gsl_matrix_column(UtY, 0);
+
+        CalcLambda('L', eval, UtW, &UtY_col.vector, cPar.l_min, cPar.l_max,
+                   cPar.n_region, cPar.l_mle_null, cPar.logl_mle_H0);
+        CalcLmmVgVeBeta(eval, UtW, &UtY_col.vector, cPar.l_mle_null,
+                        cPar.vg_mle_null, cPar.ve_mle_null, &beta.vector,
+                        &se_beta.vector);
+
+        cPar.beta_mle_null.clear();
+        cPar.se_beta_mle_null.clear();
+        for (size_t i = 0; i < B->size2; i++) {
+          cPar.beta_mle_null.push_back(gsl_matrix_get(B, 0, i));
+          cPar.se_beta_mle_null.push_back(gsl_matrix_get(se_B, 0, i));
+        }
+
+        CalcLambda('R', eval, UtW, &UtY_col.vector, cPar.l_min, cPar.l_max,
+                   cPar.n_region, cPar.l_remle_null, cPar.logl_remle_H0);
+        CalcLmmVgVeBeta(eval, UtW, &UtY_col.vector, cPar.l_remle_null,
+                        cPar.vg_remle_null, cPar.ve_remle_null, &beta.vector,
+                        &se_beta.vector);
+        cPar.beta_remle_null.clear();
+        cPar.se_beta_remle_null.clear();
+        for (size_t i = 0; i < B->size2; i++) {
+          cPar.beta_remle_null.push_back(gsl_matrix_get(B, 0, i));
+          cPar.se_beta_remle_null.push_back(gsl_matrix_get(se_B, 0, i));
+        }
+
+        CalcPve(eval, UtW, &UtY_col.vector, cPar.l_remle_null, cPar.trace_G,
+                cPar.pve_null, cPar.pve_se_null);
+        cPar.PrintSummary();
+
+        // calculate and output residuals
+        if (cPar.a_mode == 5) {
+          gsl_vector *Utu_hat = gsl_vector_alloc(Y->size1);
+          gsl_vector *Ute_hat = gsl_vector_alloc(Y->size1);
+          gsl_vector *u_hat = gsl_vector_alloc(Y->size1);
+          gsl_vector *e_hat = gsl_vector_alloc(Y->size1);
+          gsl_vector *y_hat = gsl_vector_alloc(Y->size1);
+
+          // obtain Utu and Ute
+          gsl_vector_memcpy(y_hat, &UtY_col.vector);
+          gsl_blas_dgemv(CblasNoTrans, -1.0, UtW, &beta.vector, 1.0, y_hat);
+
+          double d, u, e;
+          for (size_t i = 0; i < eval->size; i++) {
+            d = gsl_vector_get(eval, i);
+            u = cPar.l_remle_null * d / (cPar.l_remle_null * d + 1.0) *
+                gsl_vector_get(y_hat, i);
+            e = 1.0 / (cPar.l_remle_null * d + 1.0) * gsl_vector_get(y_hat, i);
+            gsl_vector_set(Utu_hat, i, u);
+            gsl_vector_set(Ute_hat, i, e);
+          }
+
+          // obtain u and e
+          gsl_blas_dgemv(CblasNoTrans, 1.0, U, Utu_hat, 0.0, u_hat);
+          gsl_blas_dgemv(CblasNoTrans, 1.0, U, Ute_hat, 0.0, e_hat);
+
+          // output residuals
+          cPar.WriteVector(u_hat, "residU");
+          cPar.WriteVector(e_hat, "residE");
+
+          gsl_vector_free(u_hat);
+          gsl_vector_free(e_hat);
+          gsl_vector_free(y_hat);
+        }
+      }
+
+      // Fit LMM or mvLMM
+      if (cPar.a_mode == 1 || cPar.a_mode == 2 || cPar.a_mode == 3 ||
+          cPar.a_mode == 4) {
+        if (cPar.n_ph == 1) {
+          LMM cLmm;
+          cLmm.CopyFromParam(cPar);
+
+          gsl_vector_view Y_col = gsl_matrix_column(Y, 0);
+          gsl_vector_view UtY_col = gsl_matrix_column(UtY, 0);
+
+          if (!cPar.file_bfile.empty()) {
+            if (cPar.file_gxe.empty()) {
+              cLmm.AnalyzePlink(U, eval, UtW, &UtY_col.vector, W,
+                                &Y_col.vector);
+            } else {
+              cLmm.AnalyzePlinkGXE(U, eval, UtW, &UtY_col.vector, W,
+                                   &Y_col.vector, env);
+            }
+          }
+          // WJA added
+          else if (!cPar.file_oxford.empty()) {
+            cLmm.Analyzebgen(U, eval, UtW, &UtY_col.vector, W, &Y_col.vector);
+          } else {
+            if (cPar.file_gxe.empty()) {
+              cLmm.AnalyzeBimbam(U, eval, UtW, &UtY_col.vector, W,
+                                 &Y_col.vector);
+            } else {
+              cLmm.AnalyzeBimbamGXE(U, eval, UtW, &UtY_col.vector, W,
+                                    &Y_col.vector, env);
+            }
+          }
+
+          cLmm.WriteFiles();
+          cLmm.CopyToParam(cPar);
+        } else {
+          MVLMM cMvlmm;
+          cMvlmm.CopyFromParam(cPar);
+
+          if (!cPar.file_bfile.empty()) {
+            if (cPar.file_gxe.empty()) {
+              cMvlmm.AnalyzePlink(U, eval, UtW, UtY);
+            } else {
+              cMvlmm.AnalyzePlinkGXE(U, eval, UtW, UtY, env);
+            }
+          } else if (!cPar.file_oxford.empty()) {
+            cMvlmm.Analyzebgen(U, eval, UtW, UtY);
+          } else {
+            if (cPar.file_gxe.empty()) {
+              cMvlmm.AnalyzeBimbam(U, eval, UtW, UtY);
+            } else {
+              cMvlmm.AnalyzeBimbamGXE(U, eval, UtW, UtY, env);
+            }
+          }
+
+          cMvlmm.WriteFiles();
+          cMvlmm.CopyToParam(cPar);
+        }
+      }
+    }
+
+    // release all matrices and vectors
+    gsl_matrix_free(Y);
+    gsl_matrix_free(W);
+    gsl_matrix_free(B);
+    gsl_matrix_free(se_B);
+    gsl_matrix_free(G);
+    gsl_matrix_free(U);
+    gsl_matrix_free(UtW);
+    gsl_matrix_free(UtY);
+    gsl_vector_free(eval);
+    gsl_vector_free(env);
+  }
+
+  // BSLMM
+  if (cPar.a_mode == 11 || cPar.a_mode == 12 || cPar.a_mode == 13) {
+    gsl_vector *y = gsl_vector_alloc(cPar.ni_test);
+    gsl_matrix *W = gsl_matrix_alloc(y->size, cPar.n_cvt);
+    gsl_matrix *G = gsl_matrix_alloc(y->size, y->size);
+    gsl_matrix *UtX = gsl_matrix_alloc(y->size, cPar.ns_test);
+
+    // set covariates matrix W and phenotype vector y
+    // an intercept should be included in W,
+    cPar.CopyCvtPhen(W, y, 0);
+
+    // center y, even for case/control data
+    cPar.pheno_mean = CenterVector(y);
+
+    // run bvsr if rho==1
+    if (cPar.rho_min == 1 && cPar.rho_max == 1) {
+      // read genotypes X (not UtX)
+      cPar.ReadGenotypes(UtX, G, false);
+
+      // perform BSLMM analysis
+      BSLMM cBslmm;
+      cBslmm.CopyFromParam(cPar);
+      time_start = clock();
+      cBslmm.MCMC(UtX, y);
+      cPar.time_opt = (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+      cBslmm.CopyToParam(cPar);
+      // else, if rho!=1
+    } else {
+      gsl_matrix *U = gsl_matrix_alloc(y->size, y->size);
+      gsl_vector *eval = gsl_vector_alloc(y->size);
+      gsl_matrix *UtW = gsl_matrix_alloc(y->size, W->size2);
+      gsl_vector *Uty = gsl_vector_alloc(y->size);
+
+      // read relatedness matrix G
+      if (!(cPar.file_kin).empty()) {
+        cPar.ReadGenotypes(UtX, G, false);
+
+        // read relatedness matrix G
+        ReadFile_kin(cPar.file_kin, cPar.indicator_idv, cPar.mapID2num,
+                     cPar.k_mode, cPar.error, G);
+        if (cPar.error == true) {
+          cout << "error! fail to read kinship/relatedness file. " << endl;
+          return;
+        }
+
+        // center matrix G
+        CenterMatrix(G);
+      } else {
+        cPar.ReadGenotypes(UtX, G, true);
+      }
+
+      // eigen-decomposition and calculate trace_G
+      cout << "Start Eigen-Decomposition..." << endl;
+      time_start = clock();
+      cPar.trace_G = EigenDecomp(G, U, eval, 0);
+      cPar.trace_G = 0.0;
+      for (size_t i = 0; i < eval->size; i++) {
+        if (gsl_vector_get(eval, i) < 1e-10) {
+          gsl_vector_set(eval, i, 0);
+        }
+        cPar.trace_G += gsl_vector_get(eval, i);
+      }
+      cPar.trace_G /= (double)eval->size;
+      cPar.time_eigen =
+          (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+
+      // calculate UtW and Uty
+      CalcUtX(U, W, UtW);
+      CalcUtX(U, y, Uty);
+
+      // calculate REMLE/MLE estimate and pve
+      CalcLambda('L', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region,
+                 cPar.l_mle_null, cPar.logl_mle_H0);
+      CalcLambda('R', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region,
+                 cPar.l_remle_null, cPar.logl_remle_H0);
+      CalcPve(eval, UtW, Uty, cPar.l_remle_null, cPar.trace_G, cPar.pve_null,
+              cPar.pve_se_null);
+
+      cPar.PrintSummary();
+
+      // Creat and calcualte UtX, use a large memory
+      cout << "Calculating UtX..." << endl;
+      time_start = clock();
+      CalcUtX(U, UtX);
+      cPar.time_UtX = (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+
+      // perform BSLMM or BSLMMDAP analysis
+      if (cPar.a_mode == 11 || cPar.a_mode == 12 || cPar.a_mode == 13) {
+        BSLMM cBslmm;
+        cBslmm.CopyFromParam(cPar);
+        time_start = clock();
+        if (cPar.a_mode == 12) { // ridge regression
+          cBslmm.RidgeR(U, UtX, Uty, eval, cPar.l_remle_null);
+        } else { // Run MCMC
+          cBslmm.MCMC(U, UtX, Uty, eval, y);
+        }
+        cPar.time_opt =
+            (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+        cBslmm.CopyToParam(cPar);
+      } else {
+      }
+
+      // release all matrices and vectors
+      gsl_matrix_free(G);
+      gsl_matrix_free(U);
+      gsl_matrix_free(UtW);
+      gsl_vector_free(eval);
+      gsl_vector_free(Uty);
+    }
+    gsl_matrix_free(W);
+    gsl_vector_free(y);
+    gsl_matrix_free(UtX);
+  }
+
+  // BSLMM-DAP
+  if (cPar.a_mode == 14 || cPar.a_mode == 15 || cPar.a_mode == 16) {
+    if (cPar.a_mode == 14) {
+      gsl_vector *y = gsl_vector_alloc(cPar.ni_test);
+      gsl_matrix *W = gsl_matrix_alloc(y->size, cPar.n_cvt);
+      gsl_matrix *G = gsl_matrix_alloc(y->size, y->size);
+      gsl_matrix *UtX = gsl_matrix_alloc(y->size, cPar.ns_test);
+
+      // set covariates matrix W and phenotype vector y
+      // an intercept should be included in W,
+      cPar.CopyCvtPhen(W, y, 0);
+
+      // center y, even for case/control data
+      cPar.pheno_mean = CenterVector(y);
+
+      // run bvsr if rho==1
+      if (cPar.rho_min == 1 && cPar.rho_max == 1) {
+        // read genotypes X (not UtX)
+        cPar.ReadGenotypes(UtX, G, false);
+
+        // perform BSLMM analysis
+        BSLMM cBslmm;
+        cBslmm.CopyFromParam(cPar);
+        time_start = clock();
+        cBslmm.MCMC(UtX, y);
+        cPar.time_opt =
+            (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+        cBslmm.CopyToParam(cPar);
+        // else, if rho!=1
+      } else {
+        gsl_matrix *U = gsl_matrix_alloc(y->size, y->size);
+        gsl_vector *eval = gsl_vector_alloc(y->size);
+        gsl_matrix *UtW = gsl_matrix_alloc(y->size, W->size2);
+        gsl_vector *Uty = gsl_vector_alloc(y->size);
+
+        // read relatedness matrix G
+        if (!(cPar.file_kin).empty()) {
+          cPar.ReadGenotypes(UtX, G, false);
+
+          // read relatedness matrix G
+          ReadFile_kin(cPar.file_kin, cPar.indicator_idv, cPar.mapID2num,
+                       cPar.k_mode, cPar.error, G);
+          if (cPar.error == true) {
+            cout << "error! fail to read kinship/relatedness file. " << endl;
+            return;
+          }
+
+          // center matrix G
+          CenterMatrix(G);
+        } else {
+          cPar.ReadGenotypes(UtX, G, true);
+        }
+
+        // eigen-decomposition and calculate trace_G
+        cout << "Start Eigen-Decomposition..." << endl;
+        time_start = clock();
+        cPar.trace_G = EigenDecomp(G, U, eval, 0);
+        cPar.trace_G = 0.0;
+        for (size_t i = 0; i < eval->size; i++) {
+          if (gsl_vector_get(eval, i) < 1e-10) {
+            gsl_vector_set(eval, i, 0);
+          }
+          cPar.trace_G += gsl_vector_get(eval, i);
+        }
+        cPar.trace_G /= (double)eval->size;
+        cPar.time_eigen =
+            (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+
+        // calculate UtW and Uty
+        CalcUtX(U, W, UtW);
+        CalcUtX(U, y, Uty);
+
+        // calculate REMLE/MLE estimate and pve
+        CalcLambda('L', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region,
+                   cPar.l_mle_null, cPar.logl_mle_H0);
+        CalcLambda('R', eval, UtW, Uty, cPar.l_min, cPar.l_max, cPar.n_region,
+                   cPar.l_remle_null, cPar.logl_remle_H0);
+        CalcPve(eval, UtW, Uty, cPar.l_remle_null, cPar.trace_G, cPar.pve_null,
+                cPar.pve_se_null);
+
+        cPar.PrintSummary();
+
+        // Creat and calcualte UtX, use a large memory
+        cout << "Calculating UtX..." << endl;
+        time_start = clock();
+        CalcUtX(U, UtX);
+        cPar.time_UtX =
+            (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+
+        // perform analysis; assume X and y are already centered
+        BSLMMDAP cBslmmDap;
+        cBslmmDap.CopyFromParam(cPar);
+        time_start = clock();
+        cBslmmDap.DAP_CalcBF(U, UtX, Uty, eval, y);
+        cPar.time_opt =
+            (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+        cBslmmDap.CopyToParam(cPar);
+
+        // release all matrices and vectors
+        gsl_matrix_free(G);
+        gsl_matrix_free(U);
+        gsl_matrix_free(UtW);
+        gsl_vector_free(eval);
+        gsl_vector_free(Uty);
+      }
+
+      gsl_matrix_free(W);
+      gsl_vector_free(y);
+      gsl_matrix_free(UtX);
+    } else if (cPar.a_mode == 15) {
+      // perform EM algorithm and estimate parameters
+      vector<string> vec_rs;
+      vector<double> vec_sa2, vec_sb2, wab;
+      vector<vector<vector<double>>> BF;
+
+      // read hyp and bf files (functions defined in BSLMMDAP)
+      ReadFile_hyb(cPar.file_hyp, vec_sa2, vec_sb2, wab);
+      ReadFile_bf(cPar.file_bf, vec_rs, BF);
+
+      cPar.ns_test = vec_rs.size();
+      if (wab.size() != BF[0][0].size()) {
+        cout << "error! hyp and bf files dimension do not match" << endl;
+      }
+
+      // load annotations
+      gsl_matrix *Ac;
+      gsl_matrix_int *Ad;
+      gsl_vector_int *dlevel;
+      size_t kc, kd;
+      if (!cPar.file_cat.empty()) {
+        ReadFile_cat(cPar.file_cat, vec_rs, Ac, Ad, dlevel, kc, kd);
+      } else {
+        kc = 0;
+        kd = 0;
+      }
+
+      cout << "## number of blocks = " << BF.size() << endl;
+      cout << "## number of analyzed SNPs = " << vec_rs.size() << endl;
+      cout << "## grid size for hyperparameters = " << wab.size() << endl;
+      cout << "## number of continuous annotations = " << kc << endl;
+      cout << "## number of discrete annotations = " << kd << endl;
+
+      // DAP_EstimateHyper (const size_t kc, const size_t kd, const
+      // vector<string> &vec_rs, const vector<double> &vec_sa2, const
+      // vector<double> &vec_sb2, const vector<double> &wab, const
+      // vector<vector<vector<double> > > &BF, gsl_matrix *Ac, gsl_matrix_int
+      // *Ad, gsl_vector_int *dlevel);
+
+      // perform analysis
+      BSLMMDAP cBslmmDap;
+      cBslmmDap.CopyFromParam(cPar);
+      time_start = clock();
+      cBslmmDap.DAP_EstimateHyper(kc, kd, vec_rs, vec_sa2, vec_sb2, wab, BF, Ac,
+                                  Ad, dlevel);
+      cPar.time_opt = (clock() - time_start) / (double(CLOCKS_PER_SEC) * 60.0);
+      cBslmmDap.CopyToParam(cPar);
+
+      gsl_matrix_free(Ac);
+      gsl_matrix_int_free(Ad);
+      gsl_vector_int_free(dlevel);
+    } else {
+      //
+    }
+  }
+
+  /*
+  //LDR (change 14 to 16?)
+  if (cPar.a_mode==14) {
+          gsl_vector *y=gsl_vector_alloc (cPar.ni_test);
+          gsl_matrix *W=gsl_matrix_alloc (y->size, cPar.n_cvt);
+          gsl_matrix *G=gsl_matrix_alloc (1, 1);
+          vector<vector<unsigned char> > Xt;
+
+          //set covariates matrix W and phenotype vector y
+          //an intercept is included in W
+          cPar.CopyCvtPhen (W, y, 0);
 
+          //read in genotype matrix X
+          cPar.ReadGenotypes (Xt, G, false);
 
+          LDR cLdr;
+          cLdr.CopyFromParam(cPar);
+          time_start=clock();
 
+          cLdr.VB(Xt, W, y);
 
-void GEMMA::WriteLog (int argc, char ** argv, PARAM &cPar)
-{
-	string file_str;
-	file_str=cPar.path_out+"/"+cPar.file_out;
-	file_str+=".log.txt";
-
-	ofstream outfile (file_str.c_str(), ofstream::out);
-	if (!outfile) {cout<<"error writing log file: "<<file_str.c_str()<<endl; return;}
-
-	outfile<<"##"<<endl;
-	outfile<<"## GEMMA Version = "<<version<<endl;
-
-	outfile<<"##"<<endl;
-	outfile<<"## Command Line Input = ";
-	for(int i = 0; i < argc; i++) {
-		outfile<<argv[i]<<" ";
-	}
-	outfile<<endl;
-
-	outfile<<"##"<<endl;
-	time_t  rawtime;
-	time(&rawtime);
-	tm *ptm = localtime (&rawtime);
-
-	outfile<<"## Date = "<<asctime(ptm);
-	  //ptm->tm_year<<":"<<ptm->tm_month<<":"<<ptm->tm_day":"<<ptm->tm_hour<<":"<<ptm->tm_min<<endl;
-
-	outfile<<"##"<<endl;
-	outfile<<"## Summary Statistics:"<<endl;
-	if (!cPar.file_cor.empty() || !cPar.file_study.empty() || !cPar.file_mstudy.empty() ) {
-	  outfile<<"## number of total individuals in the sample = "<<cPar.ni_study<<endl;
-	  outfile<<"## number of total individuals in the reference = "<<cPar.ni_ref<<endl;
-	  //outfile<<"## number of total SNPs in the sample = "<<cPar.ns_study<<endl;
-	  //outfile<<"## number of total SNPs in the reference panel = "<<cPar.ns_ref<<endl;
-	  //outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl;
-	  //outfile<<"## number of analyzed SNP pairs = "<<cPar.ns_pair<<endl;
-	  outfile<<"## number of variance components = "<<cPar.n_vc<<endl;
-
-	  outfile<<"## pve estimates = ";
-	    for (size_t i=0; i<cPar.v_pve.size(); i++) {
-	      outfile<<"  "<<cPar.v_pve[i];
-	    }
-	    outfile<<endl;
-
-	    outfile<<"## se(pve) = ";
-	    for (size_t i=0; i<cPar.v_se_pve.size(); i++) {
-	      outfile<<"  "<<cPar.v_se_pve[i];
-	    }
-	    outfile<<endl;
-
-	    if (cPar.n_vc>1) {
-	      outfile<<"## total pve = "<<cPar.pve_total<<endl;
-	      outfile<<"## se(total pve) = "<<cPar.se_pve_total<<endl;
-	    }
-
-	    outfile<<"## sigma2 per snp = ";
-	    for (size_t i=0; i<cPar.v_sigma2.size(); i++) {
-	      outfile<<"  "<<cPar.v_sigma2[i];
-	    }
-	    outfile<<endl;
-
-	    outfile<<"## se(sigma2 per snp) = ";
-	    for (size_t i=0; i<cPar.v_se_sigma2.size(); i++) {
-	      outfile<<"  "<<cPar.v_se_sigma2[i];
-	    }
-	    outfile<<endl;
-
-	    outfile<<"## enrichment = ";
-	    for (size_t i=0; i<cPar.v_enrich.size(); i++) {
-	      outfile<<"  "<<cPar.v_enrich[i];
-	    }
-	    outfile<<endl;
-
-	    outfile<<"## se(enrichment) = ";
-	    for (size_t i=0; i<cPar.v_se_enrich.size(); i++) {
-	      outfile<<"  "<<cPar.v_se_enrich[i];
-	    }
-	    outfile<<endl;
-	} else if (!cPar.file_beta.empty() && (cPar.a_mode==61 || cPar.a_mode==62) ) {
-	  outfile<<"## number of total individuals in the sample = "<<cPar.ni_study<<endl;
-	  outfile<<"## number of total individuals in the reference = "<<cPar.ni_total<<endl;
-	  outfile<<"## number of total SNPs in the sample = "<<cPar.ns_study<<endl;
-	  outfile<<"## number of total SNPs in the reference panel = "<<cPar.ns_total<<endl;
-	  outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl;
-	  outfile<<"## number of variance components = "<<cPar.n_vc<<endl;
-	} else if (!cPar.file_beta.empty() && (cPar.a_mode==66 || cPar.a_mode==67) ) {
-	  outfile<<"## number of total individuals in the sample = "<<cPar.ni_total<<endl;
-	  outfile<<"## number of total individuals in the reference = "<<cPar.ni_ref<<endl;
-	  outfile<<"## number of total SNPs in the sample = "<<cPar.ns_total<<endl;
-	  outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl;
-	  outfile<<"## number of variance components = "<<cPar.n_vc<<endl;
-
-	  outfile<<"## pve estimates = ";
-	    for (size_t i=0; i<cPar.v_pve.size(); i++) {
-	      outfile<<"  "<<cPar.v_pve[i];
-	    }
-	    outfile<<endl;
-
-	    outfile<<"## se(pve) = ";
-	    for (size_t i=0; i<cPar.v_se_pve.size(); i++) {
-	      outfile<<"  "<<cPar.v_se_pve[i];
-	    }
-	    outfile<<endl;
-
-	    if (cPar.n_vc>1) {
-	      outfile<<"## total pve = "<<cPar.pve_total<<endl;
-	      outfile<<"## se(total pve) = "<<cPar.se_pve_total<<endl;
-	    }
-
-	    outfile<<"## sigma2 per snp = ";
-	    for (size_t i=0; i<cPar.v_sigma2.size(); i++) {
-	      outfile<<"  "<<cPar.v_sigma2[i];
-	    }
-	    outfile<<endl;
-
-	    outfile<<"## se(sigma2 per snp) = ";
-	    for (size_t i=0; i<cPar.v_se_sigma2.size(); i++) {
-	      outfile<<"  "<<cPar.v_se_sigma2[i];
-	    }
-	    outfile<<endl;
-
-	    outfile<<"## enrichment = ";
-	    for (size_t i=0; i<cPar.v_enrich.size(); i++) {
-	      outfile<<"  "<<cPar.v_enrich[i];
-	    }
-	    outfile<<endl;
-
-	    outfile<<"## se(enrichment) = ";
-	    for (size_t i=0; i<cPar.v_se_enrich.size(); i++) {
-	      outfile<<"  "<<cPar.v_se_enrich[i];
-	    }
-	    outfile<<endl;
-	} else {
-	  outfile<<"## number of total individuals = "<<cPar.ni_total<<endl;
-
-	  if (cPar.a_mode==43) {
-	    outfile<<"## number of analyzed individuals = "<<cPar.ni_cvt<<endl;
-	    outfile<<"## number of individuals with full phenotypes = "<<cPar.ni_test<<endl;
-	  } else if (cPar.a_mode!=27 && cPar.a_mode!=28) {
-	    outfile<<"## number of analyzed individuals = "<<cPar.ni_test<<endl;
-	  }
-
-	  outfile<<"## number of covariates = "<<cPar.n_cvt<<endl;
-	  outfile<<"## number of phenotypes = "<<cPar.n_ph<<endl;
-	  if (cPar.a_mode==43) {
-	    outfile<<"## number of observed data = "<<cPar.np_obs<<endl;
-	    outfile<<"## number of missing data = "<<cPar.np_miss<<endl;
-	  }
-	  if (cPar.a_mode==25 || cPar.a_mode==26 || cPar.a_mode==27 || cPar.a_mode==28 || cPar.a_mode==61 || cPar.a_mode==62 || cPar.a_mode==63 || cPar.a_mode==66 || cPar.a_mode==67) {
-	    outfile<<"## number of variance components = "<<cPar.n_vc<<endl;
-	  }
-
-	  if (!(cPar.file_gene).empty()) {
-	    outfile<<"## number of total genes = "<<cPar.ng_total<<endl;
-	    outfile<<"## number of analyzed genes = "<<cPar.ng_test<<endl;
-	  } else if (cPar.file_epm.empty()) {
-	    outfile<<"## number of total SNPs = "<<cPar.ns_total<<endl;
-	    outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl;
-	  } else {
-	    outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl;
-	  }
-
-	  if (cPar.a_mode==13) {
-	    outfile<<"## number of cases = "<<cPar.ni_case<<endl;
-	    outfile<<"## number of controls = "<<cPar.ni_control<<endl;
-	  }
-	}
-
-	if ( (cPar.a_mode==61 || cPar.a_mode==62 || cPar.a_mode==63) && cPar.file_cor.empty() && cPar.file_study.empty() && cPar.file_mstudy.empty() ) {
-	    //	        outfile<<"## REMLE log-likelihood in the null model = "<<cPar.logl_remle_H0<<endl;
-	  if (cPar.n_ph==1) {
-	    outfile<<"## pve estimates = ";
-	    for (size_t i=0; i<cPar.v_pve.size(); i++) {
-	      outfile<<"  "<<cPar.v_pve[i];
-	    }
-	    outfile<<endl;
-
-	    outfile<<"## se(pve) = ";
-	    for (size_t i=0; i<cPar.v_se_pve.size(); i++) {
-	      outfile<<"  "<<cPar.v_se_pve[i];
-	    }
-	    outfile<<endl;
-
-	    if (cPar.n_vc>1) {
-	      outfile<<"## total pve = "<<cPar.pve_total<<endl;
-	      outfile<<"## se(total pve) = "<<cPar.se_pve_total<<endl;
-	    }
-
-	    outfile<<"## sigma2 estimates = ";
-	    for (size_t i=0; i<cPar.v_sigma2.size(); i++) {
-	      outfile<<"  "<<cPar.v_sigma2[i];
-	    }
-	    outfile<<endl;
-
-	    outfile<<"## se(sigma2) = ";
-	    for (size_t i=0; i<cPar.v_se_sigma2.size(); i++) {
-	      outfile<<"  "<<cPar.v_se_sigma2[i];
-	    }
-	    outfile<<endl;
-
-	    if (!cPar.file_beta.empty() ) {
-	      outfile<<"## enrichment = ";
-	      for (size_t i=0; i<cPar.v_enrich.size(); i++) {
-		outfile<<"  "<<cPar.v_enrich[i];
-	      }
-	      outfile<<endl;
-
-	      outfile<<"## se(enrichment) = ";
-	      for (size_t i=0; i<cPar.v_se_enrich.size(); i++) {
-		outfile<<"  "<<cPar.v_se_enrich[i];
-	      }
-	      outfile<<endl;
-	    }
-		  /*
-			outfile<<"## beta estimate in the null model = ";
-			for (size_t i=0; i<cPar.beta_remle_null.size(); i++) {
-				outfile<<"  "<<cPar.beta_remle_null[i];
-			}
-			outfile<<endl;
-			outfile<<"## se(beta) = ";
-			for (size_t i=0; i<cPar.se_beta_remle_null.size(); i++) {
-				outfile<<"  "<<cPar.se_beta_remle_null[i];
-			}
-			outfile<<endl;
-		  */
-	  }
-	}
-
-	if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4 || cPar.a_mode==5 || cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) {
-		outfile<<"## REMLE log-likelihood in the null model = "<<cPar.logl_remle_H0<<endl;
-		outfile<<"## MLE log-likelihood in the null model = "<<cPar.logl_mle_H0<<endl;
-		if (cPar.n_ph==1) {
-			//outfile<<"## lambda REMLE estimate in the null (linear mixed) model = "<<cPar.l_remle_null<<endl;
-			//outfile<<"## lambda MLE estimate in the null (linear mixed) model = "<<cPar.l_mle_null<<endl;
-			outfile<<"## pve estimate in the null model = "<<cPar.pve_null<<endl;
-			outfile<<"## se(pve) in the null model = "<<cPar.pve_se_null<<endl;
-			outfile<<"## vg estimate in the null model = "<<cPar.vg_remle_null<<endl;
-			outfile<<"## ve estimate in the null model = "<<cPar.ve_remle_null<<endl;
-			outfile<<"## beta estimate in the null model = ";
-			for (size_t i=0; i<cPar.beta_remle_null.size(); i++) {
-				outfile<<"  "<<cPar.beta_remle_null[i];
-			}
-			outfile<<endl;
-			outfile<<"## se(beta) = ";
-			for (size_t i=0; i<cPar.se_beta_remle_null.size(); i++) {
-				outfile<<"  "<<cPar.se_beta_remle_null[i];
-			}
-			outfile<<endl;
-
-		} else {
-			size_t c;
-			outfile<<"## REMLE estimate for Vg in the null model: "<<endl;
-			for (size_t i=0; i<cPar.n_ph; i++) {
-				for (size_t j=0; j<=i; j++) {
-					c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j);
-					outfile<<cPar.Vg_remle_null[c]<<"\t";
-				}
-				outfile<<endl;
-			}
-			outfile<<"## se(Vg): "<<endl;
-			for (size_t i=0; i<cPar.n_ph; i++) {
-				for (size_t j=0; j<=i; j++) {
-					c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j);
-					outfile<<sqrt(cPar.VVg_remle_null[c])<<"\t";
-				}
-				outfile<<endl;
-			}
-			outfile<<"## REMLE estimate for Ve in the null model: "<<endl;
-			for (size_t i=0; i<cPar.n_ph; i++) {
-				for (size_t j=0; j<=i; j++) {
-					c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j);
-					outfile<<cPar.Ve_remle_null[c]<<"\t";
-				}
-				outfile<<endl;
-			}
-			outfile<<"## se(Ve): "<<endl;
-			for (size_t i=0; i<cPar.n_ph; i++) {
-				for (size_t j=0; j<=i; j++) {
-					c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j);
-					outfile<<sqrt(cPar.VVe_remle_null[c])<<"\t";
-				}
-				outfile<<endl;
-			}
-
-			outfile<<"## MLE estimate for Vg in the null model: "<<endl;
-			for (size_t i=0; i<cPar.n_ph; i++) {
-				for (size_t j=0; j<cPar.n_ph; j++) {
-					c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j);
-					outfile<<cPar.Vg_mle_null[c]<<"\t";
-				}
-				outfile<<endl;
-			}
-			outfile<<"## se(Vg): "<<endl;
-			for (size_t i=0; i<cPar.n_ph; i++) {
-				for (size_t j=0; j<=i; j++) {
-					c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j);
-					outfile<<sqrt(cPar.VVg_mle_null[c])<<"\t";
-				}
-				outfile<<endl;
-			}
-			outfile<<"## MLE estimate for Ve in the null model: "<<endl;
-			for (size_t i=0; i<cPar.n_ph; i++) {
-				for (size_t j=0; j<cPar.n_ph; j++) {
-					c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j);
-					outfile<<cPar.Ve_mle_null[c]<<"\t";
-				}
-				outfile<<endl;
-			}
-			outfile<<"## se(Ve): "<<endl;
-			for (size_t i=0; i<cPar.n_ph; i++) {
-				for (size_t j=0; j<=i; j++) {
-					c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j);
-					outfile<<sqrt(cPar.VVe_mle_null[c])<<"\t";
-				}
-				outfile<<endl;
-			}
-			outfile<<"## estimate for B (d by c) in the null model (columns correspond to the covariates provided in the file): "<<endl;
-			for (size_t i=0; i<cPar.n_ph; i++) {
-				for (size_t j=0; j<cPar.n_cvt; j++) {
-					c=i*cPar.n_cvt+j;
-					outfile<<cPar.beta_remle_null[c]<<"\t";
-				}
-				outfile<<endl;
-			}
-			outfile<<"## se(B): "<<endl;
-			for (size_t i=0; i<cPar.n_ph; i++) {
-				for (size_t j=0; j<cPar.n_cvt; j++) {
-					c=i*cPar.n_cvt+j;
-					outfile<<cPar.se_beta_remle_null[c]<<"\t";
-				}
-				outfile<<endl;
-			}
-		}
-	}
-
-	/*
-	if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4 || cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) {
-		if (cPar.n_ph==1) {
-			outfile<<"## REMLE vg estimate in the null model = "<<cPar.vg_remle_null<<endl;
-			outfile<<"## REMLE ve estimate in the null model = "<<cPar.ve_remle_null<<endl;
-		} else {
-			size_t c;
-			outfile<<"## REMLE estimate for Vg in the null model: "<<endl;
-			for (size_t i=0; i<cPar.n_ph; i++) {
-				for (size_t j=0; j<=i; j++) {
-					c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j);
-					outfile<<cPar.Vg_remle_null[c]<<"\t";
-				}
-				outfile<<endl;
-			}
-			outfile<<"## REMLE estimate for Ve in the null model: "<<endl;
-			for (size_t i=0; i<cPar.n_ph; i++) {
-				for (size_t j=0; j<=i; j++) {
-					c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j);
-					outfile<<cPar.Ve_remle_null[c]<<"\t";
-				}
-				outfile<<endl;
-			}
-		}
-	}
-	 */
-
-
-	if (cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13 || cPar.a_mode==14 || cPar.a_mode==16) {
-	  outfile<<"## estimated mean = "<<cPar.pheno_mean<<endl;
-	}
-
-	if (cPar.a_mode==11 || cPar.a_mode==13) {
-		outfile<<"##"<<endl;
-		outfile<<"## MCMC related:"<<endl;
-		outfile<<"## initial value of h = "<<cPar.cHyp_initial.h<<endl;
-		outfile<<"## initial value of rho = "<<cPar.cHyp_initial.rho<<endl;
-		outfile<<"## initial value of pi = "<<exp(cPar.cHyp_initial.logp)<<endl;
-		outfile<<"## initial value of |gamma| = "<<cPar.cHyp_initial.n_gamma<<endl;
-		outfile<<"## random seed = "<<cPar.randseed<<endl;
-		outfile<<"## acceptance ratio = "<<(double)cPar.n_accept/(double)((cPar.w_step+cPar.s_step)*cPar.n_mh)<<endl;
-	}
-
-	outfile<<"##"<<endl;
-	outfile<<"## Computation Time:"<<endl;
-	outfile<<"## total computation time = "<<cPar.time_total<<" min "<<endl;
-	outfile<<"## computation time break down: "<<endl;
-	if (cPar.a_mode==21 || cPar.a_mode==22 || cPar.a_mode==11 || cPar.a_mode==13 || cPar.a_mode==14 || cPar.a_mode==16) {
-		outfile<<"##      time on calculating relatedness matrix = "<<cPar.time_G<<" min "<<endl;
-	}
-	if (cPar.a_mode==31) {
-		outfile<<"##      time on eigen-decomposition = "<<cPar.time_eigen<<" min "<<endl;
-	}
-	if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4 || cPar.a_mode==5 || cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13 || cPar.a_mode==14 || cPar.a_mode==16) {
-		outfile<<"##      time on eigen-decomposition = "<<cPar.time_eigen<<" min "<<endl;
-		outfile<<"##      time on calculating UtX = "<<cPar.time_UtX<<" min "<<endl;
-	}
-	if ((cPar.a_mode>=1 && cPar.a_mode<=4) || (cPar.a_mode>=51 && cPar.a_mode<=54) ) {
-		outfile<<"##      time on optimization = "<<cPar.time_opt<<" min "<<endl;
-	}
-	if (cPar.a_mode==11 || cPar.a_mode==13) {
-		outfile<<"##      time on proposal = "<<cPar.time_Proposal<<" min "<<endl;
-		outfile<<"##      time on mcmc = "<<cPar.time_opt<<" min "<<endl;
-		outfile<<"##      time on Omega = "<<cPar.time_Omega<<" min "<<endl;
-	}
-	if (cPar.a_mode==41 || cPar.a_mode==42) {
-		outfile<<"##      time on eigen-decomposition = "<<cPar.time_eigen<<" min "<<endl;
-	}
-	if (cPar.a_mode==43) {
-		outfile<<"##      time on eigen-decomposition = "<<cPar.time_eigen<<" min "<<endl;
-		outfile<<"##      time on predicting phenotypes = "<<cPar.time_opt<<" min "<<endl;
-	}
-	outfile<<"##"<<endl;
-
-	outfile.close();
-	outfile.clear();
-	return;
+          cPar.time_opt=(clock()-time_start)/(double(CLOCKS_PER_SEC)*60.0);
+          cLdr.CopyToParam(cPar);
+
+          gsl_vector_free (y);
+          gsl_matrix_free (W);
+          gsl_matrix_free (G);
+  }
+  */
+
+  cPar.time_total = (clock() - time_begin) / (double(CLOCKS_PER_SEC) * 60.0);
+
+  return;
 }
 
+void GEMMA::WriteLog(int argc, char **argv, PARAM &cPar) {
+  string file_str;
+  file_str = cPar.path_out + "/" + cPar.file_out;
+  file_str += ".log.txt";
+
+  ofstream outfile(file_str.c_str(), ofstream::out);
+  if (!outfile) {
+    cout << "error writing log file: " << file_str.c_str() << endl;
+    return;
+  }
+
+  outfile << "##" << endl;
+  outfile << "## GEMMA Version = " << version << endl;
 
+  outfile << "##" << endl;
+  outfile << "## Command Line Input = ";
+  for (int i = 0; i < argc; i++) {
+    outfile << argv[i] << " ";
+  }
+  outfile << endl;
+
+  outfile << "##" << endl;
+  time_t rawtime;
+  time(&rawtime);
+  tm *ptm = localtime(&rawtime);
+
+  outfile << "## Date = " << asctime(ptm);
+  // ptm->tm_year<<":"<<ptm->tm_month<<":"<<ptm->tm_day":"<<ptm->tm_hour<<":"<<ptm->tm_min<<endl;
+
+  outfile << "##" << endl;
+  outfile << "## Summary Statistics:" << endl;
+  if (!cPar.file_cor.empty() || !cPar.file_study.empty() ||
+      !cPar.file_mstudy.empty()) {
+    outfile << "## number of total individuals in the sample = "
+            << cPar.ni_study << endl;
+    outfile << "## number of total individuals in the reference = "
+            << cPar.ni_ref << endl;
+    // outfile<<"## number of total SNPs in the sample = "<<cPar.ns_study<<endl;
+    // outfile<<"## number of total SNPs in the reference panel =
+    // "<<cPar.ns_ref<<endl;
+    // outfile<<"## number of analyzed SNPs = "<<cPar.ns_test<<endl;
+    // outfile<<"## number of analyzed SNP pairs = "<<cPar.ns_pair<<endl;
+    outfile << "## number of variance components = " << cPar.n_vc << endl;
+
+    outfile << "## pve estimates = ";
+    for (size_t i = 0; i < cPar.v_pve.size(); i++) {
+      outfile << "  " << cPar.v_pve[i];
+    }
+    outfile << endl;
+
+    outfile << "## se(pve) = ";
+    for (size_t i = 0; i < cPar.v_se_pve.size(); i++) {
+      outfile << "  " << cPar.v_se_pve[i];
+    }
+    outfile << endl;
+
+    if (cPar.n_vc > 1) {
+      outfile << "## total pve = " << cPar.pve_total << endl;
+      outfile << "## se(total pve) = " << cPar.se_pve_total << endl;
+    }
+
+    outfile << "## sigma2 per snp = ";
+    for (size_t i = 0; i < cPar.v_sigma2.size(); i++) {
+      outfile << "  " << cPar.v_sigma2[i];
+    }
+    outfile << endl;
+
+    outfile << "## se(sigma2 per snp) = ";
+    for (size_t i = 0; i < cPar.v_se_sigma2.size(); i++) {
+      outfile << "  " << cPar.v_se_sigma2[i];
+    }
+    outfile << endl;
+
+    outfile << "## enrichment = ";
+    for (size_t i = 0; i < cPar.v_enrich.size(); i++) {
+      outfile << "  " << cPar.v_enrich[i];
+    }
+    outfile << endl;
+
+    outfile << "## se(enrichment) = ";
+    for (size_t i = 0; i < cPar.v_se_enrich.size(); i++) {
+      outfile << "  " << cPar.v_se_enrich[i];
+    }
+    outfile << endl;
+  } else if (!cPar.file_beta.empty() &&
+             (cPar.a_mode == 61 || cPar.a_mode == 62)) {
+    outfile << "## number of total individuals in the sample = "
+            << cPar.ni_study << endl;
+    outfile << "## number of total individuals in the reference = "
+            << cPar.ni_total << endl;
+    outfile << "## number of total SNPs in the sample = " << cPar.ns_study
+            << endl;
+    outfile << "## number of total SNPs in the reference panel = "
+            << cPar.ns_total << endl;
+    outfile << "## number of analyzed SNPs = " << cPar.ns_test << endl;
+    outfile << "## number of variance components = " << cPar.n_vc << endl;
+  } else if (!cPar.file_beta.empty() &&
+             (cPar.a_mode == 66 || cPar.a_mode == 67)) {
+    outfile << "## number of total individuals in the sample = "
+            << cPar.ni_total << endl;
+    outfile << "## number of total individuals in the reference = "
+            << cPar.ni_ref << endl;
+    outfile << "## number of total SNPs in the sample = " << cPar.ns_total
+            << endl;
+    outfile << "## number of analyzed SNPs = " << cPar.ns_test << endl;
+    outfile << "## number of variance components = " << cPar.n_vc << endl;
+
+    outfile << "## pve estimates = ";
+    for (size_t i = 0; i < cPar.v_pve.size(); i++) {
+      outfile << "  " << cPar.v_pve[i];
+    }
+    outfile << endl;
+
+    outfile << "## se(pve) = ";
+    for (size_t i = 0; i < cPar.v_se_pve.size(); i++) {
+      outfile << "  " << cPar.v_se_pve[i];
+    }
+    outfile << endl;
+
+    if (cPar.n_vc > 1) {
+      outfile << "## total pve = " << cPar.pve_total << endl;
+      outfile << "## se(total pve) = " << cPar.se_pve_total << endl;
+    }
+
+    outfile << "## sigma2 per snp = ";
+    for (size_t i = 0; i < cPar.v_sigma2.size(); i++) {
+      outfile << "  " << cPar.v_sigma2[i];
+    }
+    outfile << endl;
+
+    outfile << "## se(sigma2 per snp) = ";
+    for (size_t i = 0; i < cPar.v_se_sigma2.size(); i++) {
+      outfile << "  " << cPar.v_se_sigma2[i];
+    }
+    outfile << endl;
+
+    outfile << "## enrichment = ";
+    for (size_t i = 0; i < cPar.v_enrich.size(); i++) {
+      outfile << "  " << cPar.v_enrich[i];
+    }
+    outfile << endl;
+
+    outfile << "## se(enrichment) = ";
+    for (size_t i = 0; i < cPar.v_se_enrich.size(); i++) {
+      outfile << "  " << cPar.v_se_enrich[i];
+    }
+    outfile << endl;
+  } else {
+    outfile << "## number of total individuals = " << cPar.ni_total << endl;
+
+    if (cPar.a_mode == 43) {
+      outfile << "## number of analyzed individuals = " << cPar.ni_cvt << endl;
+      outfile << "## number of individuals with full phenotypes = "
+              << cPar.ni_test << endl;
+    } else if (cPar.a_mode != 27 && cPar.a_mode != 28) {
+      outfile << "## number of analyzed individuals = " << cPar.ni_test << endl;
+    }
+
+    outfile << "## number of covariates = " << cPar.n_cvt << endl;
+    outfile << "## number of phenotypes = " << cPar.n_ph << endl;
+    if (cPar.a_mode == 43) {
+      outfile << "## number of observed data = " << cPar.np_obs << endl;
+      outfile << "## number of missing data = " << cPar.np_miss << endl;
+    }
+    if (cPar.a_mode == 25 || cPar.a_mode == 26 || cPar.a_mode == 27 ||
+        cPar.a_mode == 28 || cPar.a_mode == 61 || cPar.a_mode == 62 ||
+        cPar.a_mode == 63 || cPar.a_mode == 66 || cPar.a_mode == 67) {
+      outfile << "## number of variance components = " << cPar.n_vc << endl;
+    }
+
+    if (!(cPar.file_gene).empty()) {
+      outfile << "## number of total genes = " << cPar.ng_total << endl;
+      outfile << "## number of analyzed genes = " << cPar.ng_test << endl;
+    } else if (cPar.file_epm.empty()) {
+      outfile << "## number of total SNPs = " << cPar.ns_total << endl;
+      outfile << "## number of analyzed SNPs = " << cPar.ns_test << endl;
+    } else {
+      outfile << "## number of analyzed SNPs = " << cPar.ns_test << endl;
+    }
+
+    if (cPar.a_mode == 13) {
+      outfile << "## number of cases = " << cPar.ni_case << endl;
+      outfile << "## number of controls = " << cPar.ni_control << endl;
+    }
+  }
+
+  if ((cPar.a_mode == 61 || cPar.a_mode == 62 || cPar.a_mode == 63) &&
+      cPar.file_cor.empty() && cPar.file_study.empty() &&
+      cPar.file_mstudy.empty()) {
+    //	        outfile<<"## REMLE log-likelihood in the null model =
+    //"<<cPar.logl_remle_H0<<endl;
+    if (cPar.n_ph == 1) {
+      outfile << "## pve estimates = ";
+      for (size_t i = 0; i < cPar.v_pve.size(); i++) {
+        outfile << "  " << cPar.v_pve[i];
+      }
+      outfile << endl;
+
+      outfile << "## se(pve) = ";
+      for (size_t i = 0; i < cPar.v_se_pve.size(); i++) {
+        outfile << "  " << cPar.v_se_pve[i];
+      }
+      outfile << endl;
+
+      if (cPar.n_vc > 1) {
+        outfile << "## total pve = " << cPar.pve_total << endl;
+        outfile << "## se(total pve) = " << cPar.se_pve_total << endl;
+      }
+
+      outfile << "## sigma2 estimates = ";
+      for (size_t i = 0; i < cPar.v_sigma2.size(); i++) {
+        outfile << "  " << cPar.v_sigma2[i];
+      }
+      outfile << endl;
+
+      outfile << "## se(sigma2) = ";
+      for (size_t i = 0; i < cPar.v_se_sigma2.size(); i++) {
+        outfile << "  " << cPar.v_se_sigma2[i];
+      }
+      outfile << endl;
+
+      if (!cPar.file_beta.empty()) {
+        outfile << "## enrichment = ";
+        for (size_t i = 0; i < cPar.v_enrich.size(); i++) {
+          outfile << "  " << cPar.v_enrich[i];
+        }
+        outfile << endl;
+
+        outfile << "## se(enrichment) = ";
+        for (size_t i = 0; i < cPar.v_se_enrich.size(); i++) {
+          outfile << "  " << cPar.v_se_enrich[i];
+        }
+        outfile << endl;
+      }
+      /*
+            outfile<<"## beta estimate in the null model = ";
+            for (size_t i=0; i<cPar.beta_remle_null.size(); i++) {
+                    outfile<<"  "<<cPar.beta_remle_null[i];
+            }
+            outfile<<endl;
+            outfile<<"## se(beta) = ";
+            for (size_t i=0; i<cPar.se_beta_remle_null.size(); i++) {
+                    outfile<<"  "<<cPar.se_beta_remle_null[i];
+            }
+            outfile<<endl;
+      */
+    }
+  }
+
+  if (cPar.a_mode == 1 || cPar.a_mode == 2 || cPar.a_mode == 3 ||
+      cPar.a_mode == 4 || cPar.a_mode == 5 || cPar.a_mode == 11 ||
+      cPar.a_mode == 12 || cPar.a_mode == 13) {
+    outfile << "## REMLE log-likelihood in the null model = "
+            << cPar.logl_remle_H0 << endl;
+    outfile << "## MLE log-likelihood in the null model = " << cPar.logl_mle_H0
+            << endl;
+    if (cPar.n_ph == 1) {
+      // outfile<<"## lambda REMLE estimate in the null (linear mixed) model =
+      // "<<cPar.l_remle_null<<endl;
+      // outfile<<"## lambda MLE estimate in the null (linear mixed) model =
+      // "<<cPar.l_mle_null<<endl;
+      outfile << "## pve estimate in the null model = " << cPar.pve_null
+              << endl;
+      outfile << "## se(pve) in the null model = " << cPar.pve_se_null << endl;
+      outfile << "## vg estimate in the null model = " << cPar.vg_remle_null
+              << endl;
+      outfile << "## ve estimate in the null model = " << cPar.ve_remle_null
+              << endl;
+      outfile << "## beta estimate in the null model = ";
+      for (size_t i = 0; i < cPar.beta_remle_null.size(); i++) {
+        outfile << "  " << cPar.beta_remle_null[i];
+      }
+      outfile << endl;
+      outfile << "## se(beta) = ";
+      for (size_t i = 0; i < cPar.se_beta_remle_null.size(); i++) {
+        outfile << "  " << cPar.se_beta_remle_null[i];
+      }
+      outfile << endl;
+
+    } else {
+      size_t c;
+      outfile << "## REMLE estimate for Vg in the null model: " << endl;
+      for (size_t i = 0; i < cPar.n_ph; i++) {
+        for (size_t j = 0; j <= i; j++) {
+          c = (2 * cPar.n_ph - min(i, j) + 1) * min(i, j) / 2 + max(i, j) -
+              min(i, j);
+          outfile << cPar.Vg_remle_null[c] << "\t";
+        }
+        outfile << endl;
+      }
+      outfile << "## se(Vg): " << endl;
+      for (size_t i = 0; i < cPar.n_ph; i++) {
+        for (size_t j = 0; j <= i; j++) {
+          c = (2 * cPar.n_ph - min(i, j) + 1) * min(i, j) / 2 + max(i, j) -
+              min(i, j);
+          outfile << sqrt(cPar.VVg_remle_null[c]) << "\t";
+        }
+        outfile << endl;
+      }
+      outfile << "## REMLE estimate for Ve in the null model: " << endl;
+      for (size_t i = 0; i < cPar.n_ph; i++) {
+        for (size_t j = 0; j <= i; j++) {
+          c = (2 * cPar.n_ph - min(i, j) + 1) * min(i, j) / 2 + max(i, j) -
+              min(i, j);
+          outfile << cPar.Ve_remle_null[c] << "\t";
+        }
+        outfile << endl;
+      }
+      outfile << "## se(Ve): " << endl;
+      for (size_t i = 0; i < cPar.n_ph; i++) {
+        for (size_t j = 0; j <= i; j++) {
+          c = (2 * cPar.n_ph - min(i, j) + 1) * min(i, j) / 2 + max(i, j) -
+              min(i, j);
+          outfile << sqrt(cPar.VVe_remle_null[c]) << "\t";
+        }
+        outfile << endl;
+      }
+
+      outfile << "## MLE estimate for Vg in the null model: " << endl;
+      for (size_t i = 0; i < cPar.n_ph; i++) {
+        for (size_t j = 0; j < cPar.n_ph; j++) {
+          c = (2 * cPar.n_ph - min(i, j) + 1) * min(i, j) / 2 + max(i, j) -
+              min(i, j);
+          outfile << cPar.Vg_mle_null[c] << "\t";
+        }
+        outfile << endl;
+      }
+      outfile << "## se(Vg): " << endl;
+      for (size_t i = 0; i < cPar.n_ph; i++) {
+        for (size_t j = 0; j <= i; j++) {
+          c = (2 * cPar.n_ph - min(i, j) + 1) * min(i, j) / 2 + max(i, j) -
+              min(i, j);
+          outfile << sqrt(cPar.VVg_mle_null[c]) << "\t";
+        }
+        outfile << endl;
+      }
+      outfile << "## MLE estimate for Ve in the null model: " << endl;
+      for (size_t i = 0; i < cPar.n_ph; i++) {
+        for (size_t j = 0; j < cPar.n_ph; j++) {
+          c = (2 * cPar.n_ph - min(i, j) + 1) * min(i, j) / 2 + max(i, j) -
+              min(i, j);
+          outfile << cPar.Ve_mle_null[c] << "\t";
+        }
+        outfile << endl;
+      }
+      outfile << "## se(Ve): " << endl;
+      for (size_t i = 0; i < cPar.n_ph; i++) {
+        for (size_t j = 0; j <= i; j++) {
+          c = (2 * cPar.n_ph - min(i, j) + 1) * min(i, j) / 2 + max(i, j) -
+              min(i, j);
+          outfile << sqrt(cPar.VVe_mle_null[c]) << "\t";
+        }
+        outfile << endl;
+      }
+      outfile << "## estimate for B (d by c) in the null model (columns "
+                 "correspond to the covariates provided in the file): "
+              << endl;
+      for (size_t i = 0; i < cPar.n_ph; i++) {
+        for (size_t j = 0; j < cPar.n_cvt; j++) {
+          c = i * cPar.n_cvt + j;
+          outfile << cPar.beta_remle_null[c] << "\t";
+        }
+        outfile << endl;
+      }
+      outfile << "## se(B): " << endl;
+      for (size_t i = 0; i < cPar.n_ph; i++) {
+        for (size_t j = 0; j < cPar.n_cvt; j++) {
+          c = i * cPar.n_cvt + j;
+          outfile << cPar.se_beta_remle_null[c] << "\t";
+        }
+        outfile << endl;
+      }
+    }
+  }
+
+  /*
+  if (cPar.a_mode==1 || cPar.a_mode==2 || cPar.a_mode==3 || cPar.a_mode==4 ||
+  cPar.a_mode==11 || cPar.a_mode==12 || cPar.a_mode==13) {
+          if (cPar.n_ph==1) {
+                  outfile<<"## REMLE vg estimate in the null model =
+  "<<cPar.vg_remle_null<<endl;
+                  outfile<<"## REMLE ve estimate in the null model =
+  "<<cPar.ve_remle_null<<endl;
+          } else {
+                  size_t c;
+                  outfile<<"## REMLE estimate for Vg in the null model: "<<endl;
+                  for (size_t i=0; i<cPar.n_ph; i++) {
+                          for (size_t j=0; j<=i; j++) {
+                                  c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j);
+                                  outfile<<cPar.Vg_remle_null[c]<<"\t";
+                          }
+                          outfile<<endl;
+                  }
+                  outfile<<"## REMLE estimate for Ve in the null model: "<<endl;
+                  for (size_t i=0; i<cPar.n_ph; i++) {
+                          for (size_t j=0; j<=i; j++) {
+                                  c=(2*cPar.n_ph-min(i,j)+1)*min(i,j)/2+max(i,j)-min(i,j);
+                                  outfile<<cPar.Ve_remle_null[c]<<"\t";
+                          }
+                          outfile<<endl;
+                  }
+          }
+  }
+   */
+
+  if (cPar.a_mode == 11 || cPar.a_mode == 12 || cPar.a_mode == 13 ||
+      cPar.a_mode == 14 || cPar.a_mode == 16) {
+    outfile << "## estimated mean = " << cPar.pheno_mean << endl;
+  }
+
+  if (cPar.a_mode == 11 || cPar.a_mode == 13) {
+    outfile << "##" << endl;
+    outfile << "## MCMC related:" << endl;
+    outfile << "## initial value of h = " << cPar.cHyp_initial.h << endl;
+    outfile << "## initial value of rho = " << cPar.cHyp_initial.rho << endl;
+    outfile << "## initial value of pi = " << exp(cPar.cHyp_initial.logp)
+            << endl;
+    outfile << "## initial value of |gamma| = " << cPar.cHyp_initial.n_gamma
+            << endl;
+    outfile << "## random seed = " << cPar.randseed << endl;
+    outfile << "## acceptance ratio = "
+            << (double)cPar.n_accept /
+                   (double)((cPar.w_step + cPar.s_step) * cPar.n_mh)
+            << endl;
+  }
+
+  outfile << "##" << endl;
+  outfile << "## Computation Time:" << endl;
+  outfile << "## total computation time = " << cPar.time_total << " min "
+          << endl;
+  outfile << "## computation time break down: " << endl;
+  if (cPar.a_mode == 21 || cPar.a_mode == 22 || cPar.a_mode == 11 ||
+      cPar.a_mode == 13 || cPar.a_mode == 14 || cPar.a_mode == 16) {
+    outfile << "##      time on calculating relatedness matrix = "
+            << cPar.time_G << " min " << endl;
+  }
+  if (cPar.a_mode == 31) {
+    outfile << "##      time on eigen-decomposition = " << cPar.time_eigen
+            << " min " << endl;
+  }
+  if (cPar.a_mode == 1 || cPar.a_mode == 2 || cPar.a_mode == 3 ||
+      cPar.a_mode == 4 || cPar.a_mode == 5 || cPar.a_mode == 11 ||
+      cPar.a_mode == 12 || cPar.a_mode == 13 || cPar.a_mode == 14 ||
+      cPar.a_mode == 16) {
+    outfile << "##      time on eigen-decomposition = " << cPar.time_eigen
+            << " min " << endl;
+    outfile << "##      time on calculating UtX = " << cPar.time_UtX << " min "
+            << endl;
+  }
+  if ((cPar.a_mode >= 1 && cPar.a_mode <= 4) ||
+      (cPar.a_mode >= 51 && cPar.a_mode <= 54)) {
+    outfile << "##      time on optimization = " << cPar.time_opt << " min "
+            << endl;
+  }
+  if (cPar.a_mode == 11 || cPar.a_mode == 13) {
+    outfile << "##      time on proposal = " << cPar.time_Proposal << " min "
+            << endl;
+    outfile << "##      time on mcmc = " << cPar.time_opt << " min " << endl;
+    outfile << "##      time on Omega = " << cPar.time_Omega << " min " << endl;
+  }
+  if (cPar.a_mode == 41 || cPar.a_mode == 42) {
+    outfile << "##      time on eigen-decomposition = " << cPar.time_eigen
+            << " min " << endl;
+  }
+  if (cPar.a_mode == 43) {
+    outfile << "##      time on eigen-decomposition = " << cPar.time_eigen
+            << " min " << endl;
+    outfile << "##      time on predicting phenotypes = " << cPar.time_opt
+            << " min " << endl;
+  }
+  outfile << "##" << endl;
+
+  outfile.close();
+  outfile.clear();
+  return;
+}