Bump to version 1.0.3

CMakeLists.txt

@@ -1,5 +1,5 @@
 cmake_minimum_required(VERSION 3.7.2)
-project(LEMMA VERSION 1.0.2)
+project(LEMMA VERSION 1.0.3)
 
 set(CMAKE_CXX_STANDARD 11)
 

docs/gplemma.md

@@ -18,7 +18,7 @@ The phenotype has been simulated to have:
 ## Getting started
 To run the GPLEMMA method on the dataset given above, run the following commands
 ```
-mpirun -n 1 build/lemma_1_0_1 \
+mpirun -n 1 build/lemma_1_0_3 \
   --gplemma --random-seed 1 \
   --pheno example/pheno.txt.gz \
   --streamBgen example/n5k_p20k_example.bgen \
@@ -29,7 +29,7 @@ This should return heritability estimates of h2-G = 0.229 (0.028) and h2-GxE = 0
 
 To run the MEMMA method on the same dataset, run the following commands
 ```
-mpirun -n 1 build/lemma_1_0_1 \
+mpirun -n 1 build/lemma_1_0_3 \
   --RHEreg --random-seed 1 \
   --pheno example/pheno.txt.gz \
   --streamBgen example/n5k_p20k_example.bgen \

docs/lemma.md

@@ -29,7 +29,7 @@ for cc in `seq 1 22`; do
   echo "example/n5k_p20k_example_chr${cc}.bgen" >> example/bgen_filenames.txt;
 done
 
-mpirun -n 1 build/lemma_1_0_1 \
+mpirun -n 1 build/lemma_1_0_3 \
   --pheno example/pheno.txt.gz \
   --environment example/env.txt.gz \
   --VB \
@@ -63,7 +63,7 @@ The LEMMA algorithm is modular, and so each step can be performed separately as
 
 ### Running the LEMMA variational inference algorithm
 ```
-mpirun -n 1 build/lemma_1_0_1 \
+mpirun -n 1 build/lemma_1_0_3 \
   --VB \
   --pheno example/pheno.txt.gz \
   --environment example/env.txt.gz \
@@ -74,7 +74,7 @@ In this case the algorithm should converge in 59 iterations.
 
 ### Association testing with imputed SNPs
 ```
-mpirun -n 1 build/lemma_1_0_1 \
+mpirun -n 1 build/lemma_1_0_3 \
   --singleSnpStats --maf 0.01 \
   --pheno example/pheno.txt.gz \
   --resid-pheno example/inference_converged_yhat.out.gz \
@@ -88,7 +88,7 @@ For analyses of large genomic datasets it may be useful to parallelize associati
 
 ### Heritability estimation
 ```
-mpirun -n 1 build/lemma_1_0_1 \
+mpirun -n 1 build/lemma_1_0_3 \
   --RHEreg --random-seed 1 \
   --pheno example/pheno.txt.gz \
   --mStreamBgen example/bgen_filenames.txt \
@@ -122,7 +122,7 @@ zcat example/dxteex_chr*.out.gz > example/dxteex.out.gz
 ```
 Then provide the file `example/dxteex.out.gz` to LEMMA with the commandline flag `--dxteex`.
 ```
-mpirun -n 1 build/lemma_1_0_1 \
+mpirun -n 1 build/lemma_1_0_3 \
   --VB \
   --pheno example/pheno.txt.gz \
   --environment example/env.txt.gz \
@@ -141,7 +141,7 @@ To convert into the file format expected by LEMMA we have provided a brief Rscri
 
 Then run the heritability analysis as follows
 ```
-mpirun -n 1 build/lemma_1_0_1 \
+mpirun -n 1 build/lemma_1_0_3 \
   --RHEreg --n-RHEreg-samples 20 --n-RHEreg-jacknife 100 --random-seed 1 \
   --pheno example/pheno.txt.gz \
   --bgen example/n5k_p20k_example.bgen \
@@ -153,15 +153,15 @@ mpirun -n 1 build/lemma_1_0_1 \
 ### Resuming from a previous parameter state
 In case of runtime crashes, LEMMA can save the parameter state at periodic intervals by providing the commandline flag `--resume-from-state`. LEMMA can then subsequently resume inference from this saved state. For example
 ```
-mpirun -n 1 build/lemma_1_0_1 \
+mpirun -n 1 build/lemma_1_0_3 \
   --VB \
   --pheno example/pheno.txt.gz \
   --environment example/env.txt.gz \
   --bgen example/n5k_p20k_example.bgen \
   --param_dump_interval 10 \
   --out example/inference.out.gz
 
-mpirun -n 1 build/lemma_1_0_1 \
+mpirun -n 1 build/lemma_1_0_3 \
   --VB \
   --pheno example/pheno.txt.gz \
   --environment example/env.txt.gz \