Update README.md

README.md

@@ -24,7 +24,8 @@ Description: Package for Breed Wheat Genomic Selection pipeline
 
 **Date of Publication:**
 
-##R topics documented:
+## R topics documented:  
+
 1. [AM - Additive relationship matrix](#am)
 2. [ANO - Selectio of N markers with the lowest Pvalues in GWAS](#ano)
 3. [bwgs.cv - BreedWheat Genomic Selection Cross Validation](#bwgscv)
@@ -38,7 +39,7 @@ Description: Package for Breed Wheat Genomic Selection pipeline
 11. [RMR - Random Marker Recontruction](#rmr)
 12. [RPS - Random Pop Size](#rps)
  
-##<a name="am"></a> AM - Additive relationship matrix
+## <a name="am"></a> AM - Additive relationship matrix
 
 **Description**
 
@@ -79,7 +80,7 @@ realized relationship matrix.
 
 G3:Genes, Genomes, Genetics. 2:1405-1413. doi: 10.1534/g3.112.004259
  
-##<a name="ano"></a> ANO -  selection of N markers with the lowest Pvalue in GWAS
+## <a name="ano"></a> ANO -  selection of N markers with the lowest Pvalue in GWAS
  
 
 **Description**
@@ -120,7 +121,7 @@ geno_impote <- MNI (geno47K)
 geno_shrink001 <- ANO(pheno, geno_impute, pval=0.001)
 ```
 
-##<a name="bwgscv"></a>  bwgs.cv -  Breed Wheat Genomic Selection Cross Validation
+## <a name="bwgscv"></a>  bwgs.cv -  Breed Wheat Genomic Selection Cross Validation
 
 **Description**
 
@@ -273,7 +274,7 @@ testRKHSANO<bwgs.cv(geno47K,pheno,geno.reduct.method="ANO",pval=0.001,geno.imput
 
 **References**
 
-Breiman, L. 2001. \"Random Forests\". Machine Learning 45 (1): 5--32.
+Breiman, L. 2001. "Random Forests". Machine Learning 45 (1): 5--32.
 
 Breiman L. and Cutler A. (2013) Breiman and Cutler's random forests for
 classification and regression.
@@ -323,7 +324,7 @@ Zou H and Hastie T (2005) Regularization and variable selection via the
 elastic net. J. R. Statist. Soc. 67: 301--320
 
 
-##<a name="bwgspredict"></a>  bwgs.predict   Breed Wheat Genomic Selection Prediction
+## <a name="bwgspredict"></a>  bwgs.predict   Breed Wheat Genomic Selection Prediction
 
 **Description**
 
@@ -448,7 +449,7 @@ cor
 cbind((testPREDICT_GBLUP[,1],testPREDICT_EGBLUP[,1],testPREDICT_BA[,1]))
 ```
 
-##<a name="chromld"></a> CHROMLD -  Marker selection by LD pruning within each chromosome
+## <a name="chromld"></a> CHROMLD -  Marker selection by LD pruning within each chromosome
 
 
 **Description**
@@ -496,7 +497,7 @@ data(inra)
 genoLD95 <- CHROMLD(geno47K, R2seuil=0.95, MAP)
 ```
 
-##<a name="emi"></a>  EMI -  Expectation-Maximization Imputation
+## <a name="emi"></a>  EMI -  Expectation-Maximization Imputation
 
 
 **Description**
@@ -553,7 +554,7 @@ Y., Dreisigacker, S., Crossa, J., Sanchez-Villeda, H., Sorrells, M.E., &
 Jannink, J. (2012). Genomic Selection in Wheat Breeding using
 Genotyping-by-Sequencing.
 
-##<a name="inra"></a> inra -  data from INRA breeding data, genotype and phenotype
+## <a name="inra"></a> inra -  data from INRA breeding data, genotype and phenotype
 
 
 **Description**
@@ -566,7 +567,7 @@ inra data contains a set of geno47K(760 x 47839), pheno (760 x 1). The phenotype
 data(inra)
 ```
 
-##<a name="mni"></a>  MNI -  MeaN Impute
+## <a name="mni"></a>  MNI -  MeaN Impute
 
 
 **Description**
@@ -603,7 +604,7 @@ data(inra)
 geno_MNI <- MNI(geno47K)
 ```
 
-##<a name="optitrain"></a> optiTRAIN -  Optimization of TRAINING set by CDmean
+## <a name="optitrain"></a> optiTRAIN -  Optimization of TRAINING set by CDmean
 
 **Description**
 
@@ -652,7 +653,7 @@ Train_opti300 <- optiTRAIN (geno47K, 300, 1000)
 
 Rincent, R., D. Laloe, S. Nicolas, T. Altmann, D. Brunel, P. Revilla, V., M Rodriguez J. Moreno-Gonzalez, A. Melchinger, E. Bauer, C.C. Schoen, N. Meyer, C. Giauffret, C. Bauland, P. Jamin, J. Laborde, H. Monod, P. Flament, A. Charcosset, and L. Moreau. 2012. Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: Comparison of methods in two diverse groups of maize inbreds (Zea may L.) Genetics 192:715--728 doi:10.1534/genetics.112.141473
 
-##<a name="qtlsim"></a> qtlSIM -  Simulation of QTL
+## <a name="qtlsim"></a> qtlSIM -  Simulation of QTL
 
 
 **Description**
@@ -702,7 +703,7 @@ data(inra)
 TestQTL = qtlSIM (geno47K, NQLT=100, h2=0.3)
 ```
 
-##<a name="rmr"></a>  RMR -  Random Marker Reconstruction
+## <a name="rmr"></a>  RMR -  Random Marker Reconstruction
  
 
 **Description**
@@ -741,7 +742,7 @@ data(inra)
 Geno5K <- RMR(geno47K, 5000)
 ```
 
-##<a name="rps"></a>  RPS -  Random Pop Size
+## <a name="rps"></a>  RPS -  Random Pop Size
 
 **Description**