diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..cd67eac
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.Rproj.user
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..a5d03bd
--- /dev/null
+++ b/README.md
@@ -0,0 +1,377 @@
+
+# MMGS
+A R package MMGS is developed by Mingjia Zhu. You can use this packages for Genomic selection across different envs whithin different model, such as rrBLUP, bayesian, LightGBM and so on.
+
+## Description
+
+A  devlopment R packages used for GS within diff envs.
+
+## Installation
+
+You can install the package from CRAN using the following command:
+From Github:
+```R
+devtools::install_github("Ryougi-yukiro/MMGS")
+```
+
+## Usage
+Provide examples of how to use your package. Include code snippets and brief explanations to demonstrate the key features and functionalities.
+You can also provide links to additional resources or documentation.
+
+### Step.1 Load packages and data
+```R
+#Load the required packages
+library("MMGS")
+library("dplyr")# used for data reshape and melt
+
+#Load the input data
+trait<-read.table(file="Trait_records.txt",header=T)
+env_info<-read.table(file="Env_meta_table.txt",header=T)
+PTT_PTR <- read.table(file="7Envs_envParas_DAP122.txt", header = T , sep = "\t")
+geno <- read.table(file="Genotype.txt", header = T , sep = "\t")
+```
+### Step.2 estimate h2 within different Envs
+```R
+h2<-h2_rrBLUP(trait=trait,geno=geno,envs="env_code")
+#if you need estiamte H2 plz use asreml or VCA package
+#example code as follow:
+trait$Loc <- as.factor(trait$env_code)
+trait$GDD <- as.numeric(as.character(trait$PH))
+trait$Entry <- gsub("E", "", trait$line_code)
+
+Loc=as.character(trait$Loc);
+Entry=as.character(trait$Entry);
+Rep=as.character(trait$pop_code);
+y=trait$GDD;
+
+mydataframe=data.frame(y,Loc,Entry,Rep);
+fit_r <-remlMM(y ~ (Loc)+(Entry)+(Loc/Rep)+(Loc*Entry), mydataframe)
+GxE_Var=fit_r$aov.tab[5,2];
+G_Var=fit_r$aov.tab[3,2];
+Error_Var=fit_r$aov.tab[6,2];
+fit_a <-anovaMM(y ~ Loc+Loc/Rep+Entry+Loc*Entry, mydataframe)
+fit_a
+EN=unique(Loc);
+GenoVarInd=numeric();
+for(i in 1:7)
+{
+  data1=mydataframe[mydataframe$Loc==EN[1],];
+  data1=data1[which(!(is.na(data1$y))),];
+  fit1 <-remlMM(y ~ (Entry)+(Rep), data1)
+  print(fit1)
+  #Variance component for genotypes###
+  GenoVar=fit1$aov.tab[2,2];
+  GenoVarInd=c(GenoVarInd,GenoVar)
+}
+#V
+V=sum((sqrt(GenoVarInd)-mean(sqrt(GenoVarInd)))^2)/(length(EN)-1)
+V.percent=V/GxE_Var;
+V
+V.percent
+#L
+L=GxE_Var-V;
+L.percent=L/GxE_Var;
+L
+L.percent
+
+#Line/Entry mean heritability
+h2=G_Var/(G_Var+GxE_Var/length(EN)+Error_Var/(length(EN)*2));
+h2
+#Pooled genetic correlation
+rg=G_Var/(G_Var+L)
+rg
+```
+### Step.2 data analysis 
+```R
+env_trait<-env_trait_calculate(data=trait,trait="FTgdd",env="env_code")
+LbyE<-LbyE_calculate(data=trait,trait="FTgdd",env="env_code",line="line_code")
+#envs similarity, plot by ggplot2 like heatmap
+LbyE_corrplot(LbyE=LbyE,cor_type="phetamp",color=c("blue","white","red"))
+
+#Get lines mean and Q25, Q75 within each envs
+etl<-etl_calculate(data=env_info,trait="FTgdd",env="env_code",bycol="lat")
+etl_plotter(data=etl,mean=env_trait) # you could design your plotter use more paras
+
+#Get Best Days window and Env Paras
+Paras <- c('DL', 'GDD', 'PTT', 'PTR', 'PTS');
+#p dap_x dap_y searching_daps according to your data
+pop_cor<-Exhaustive_search(data=env_trait, env_paras=PTT_PTR, searching_daps=80,
+                           p=1, dap_x=80,dap_y=80,LOO=0,Paras=Paras)
+#Result Visualization
+Exhaustive_plotter(data=pop_cor,dap_x=80, dap_y=80,p=1)
+
+#Get Envs mean within env paras
+envMeanPara<-envMeanPara(data=env_trait, env_paras=PTT_PTR, maxR_dap1=18,maxR_dap2=43, Paras=Paras)
+```
+
+### Step.3 CV
+Users can customize the model they need, the function uses the norm reaction by default, the given environment parameters can be obtained from the previous results , fold number represents the number of folds, reshuffle represents the number of repetitions. In RM.G mode, the available models are rrBLUP, LASSO,EN,RR,BA,BB,BC,BL,BRR,RKHS,MKRKHS,SVM,RF and LightGBM.
+```R
+#Check pheno
+pheno<-LbyE[which(as.character(LbyE$line_code)%in%c("line_code",as.character(geno$line_code))),];
+#CV 
+out<-GE_CV(pheno=pheno, geno=geno, env=env_info,
+             para=envMeanPara, Para_Name="PTT", depend="norm",
+             model="rrBLUP", fold=2, reshuffle=5, methods="RM.G")
+#result
+#> mean(out[[3]])
+#[1] 0.8728506
+#> apply(out[[2]],2,mean)
+#     PR12      IA14      PR11      IA13     PR14S      KS11      KS12 
+#0.5418663 0.3868576 0.5381628 0.4759335 0.4871427 0.6219213 0.6380658
+#head(out[[1]])
+#       obs      pre     col para
+#1 1595.988 1588.782 #FF0000 PR12
+#2 1512.918 1576.437 #FF0000 PR12
+```
+
+### Others function Example
+```R
+result<-line_trait_mean(data=trait,trait="FTgdd",mean=env_trait,LbyE=LbyE,row=2)
+MSE<-result[[1]]
+ltm<-result[[2]]
+mse_plotter(MSE)
+
+Reg<-Reg(LbyE=LbyE,env_trait=env_trait)
+Reg_plotter(Reg=Reg)
+Mean_trait_plot(Reg,MSE)
+
+Slope_Intercept<-Slope_Intercept(data=filtered_trait,input=env_trait, env_paras=PTT_PTR,
+                  Para_Name="PTS",line="line_code",trait="PH",filter=5,
+                  maxR_dap1=22, maxR_dap2=35,rounds=4)
+
+prdM <- LOOCV(maxR_dap1=22,maxR_dap2=35,data=filtered_trait,input=env_trait,env_paras=PTT_PTR,
+              Para_Name="PTS",trait="PH",line="line_code",p=1,filter=4)
+
+prdM_plotter(prdM=prdM,data=envMeanPara,trait="PH",Para_Name="PTS");
+envMeanPara_plotter(envMeanPara)
+```
+
+## Documentation
+See full documentation from original repository
+
+## Command line interface
+* env_trait_calculate
+* envMeanPara
+* envMeanPara_plotter
+* etl_calculate
+* etl_plotter
+* Exhaustive_plotter
+* Exhaustive_search
+* GE_CV
+* h2_rrBLUP
+* LbyE_calculate
+* LbyE_corrplot
+* line_trait_mean
+* LOOCV
+* ltm_plotter
+* Mean_trait_plot
+* mse_plotter
+* prdM_plotter
+* Reg
+* Reg_plotter
+* Slope_Intercept
+
+## Implementation notes
+ggGSE is a collection of tools for cross-environmental genome-wide selection prediction that integrates most genome-wide prediction models, both parametric and non-parametric. You can input your own collected data against sample data and get the results you want directly through the built-in functions of the toolkit, which requires no additional statistical knowledge or coding skills and is somewhat user-friendly because it saves users from having to search for various tools and apply them to cross-environmental prediction.
+
+## License
+[GPL-3](https://www.gnu.org/licenses/quick-guide-gplv3.html)
+
+## Reference
+[Li X, Guo T, Wang J, et al. An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops[J]. Molecular Plant](https://www.sciencedirect.com/science/article/pii/S167420522100085X)
+
+[Jarquín D, Crossa J, Lacaze X, et al. A reaction norm model for genomic selection using high-dimensional genomic and environmental data[J]. Theoretical and applied genetics, 2014, 127: 595-607.](https://link.springer.com/article/10.1007/s00122-013-2243-1)
+
+
+
+
+=======
+# MMGS
+A R package MMGS is developed by Mingjia Zhu. You can use this packages for Genomic selection across different envs whithin different model, such as rrBLUP, bayesian, LightGBM and so on.
+
+## Description
+
+A  devlopment R packages used for GS within diff envs.
+
+## Installation
+
+You can install the package from CRAN using the following command:
+From Github:
+```R
+devtools::install_github("Ryougi-yukiro/MMGS")
+```
+
+## Usage
+Provide examples of how to use your package. Include code snippets and brief explanations to demonstrate the key features and functionalities.
+You can also provide links to additional resources or documentation.
+
+### Step.1 Load packages and data
+```R
+#Load the required packages
+library("MMGS")
+library("dplyr")# used for data reshape and melt
+
+#Load the input data
+trait<-read.table(file="Trait_records.txt",header=T)
+env_info<-read.table(file="Env_meta_table.txt",header=T)
+PTT_PTR <- read.table(file="7Envs_envParas_DAP122.txt", header = T , sep = "\t")
+geno <- read.table(file="Genotype.txt", header = T , sep = "\t")
+```
+### Step.2 estimate h2 within different Envs
+```R
+h2<-h2_rrBLUP(trait=trait,geno=geno,envs="env_code")
+#if you need estiamte H2 plz use asreml or VCA package
+#example code as follow:
+trait$Loc <- as.factor(trait$env_code)
+trait$GDD <- as.numeric(as.character(trait$PH))
+trait$Entry <- gsub("E", "", trait$line_code)
+
+Loc=as.character(trait$Loc);
+Entry=as.character(trait$Entry);
+Rep=as.character(trait$pop_code);
+y=trait$GDD;
+
+mydataframe=data.frame(y,Loc,Entry,Rep);
+fit_r <-remlMM(y ~ (Loc)+(Entry)+(Loc/Rep)+(Loc*Entry), mydataframe)
+GxE_Var=fit_r$aov.tab[5,2];
+G_Var=fit_r$aov.tab[3,2];
+Error_Var=fit_r$aov.tab[6,2];
+fit_a <-anovaMM(y ~ Loc+Loc/Rep+Entry+Loc*Entry, mydataframe)
+fit_a
+EN=unique(Loc);
+GenoVarInd=numeric();
+for(i in 1:7)
+{
+  data1=mydataframe[mydataframe$Loc==EN[1],];
+  data1=data1[which(!(is.na(data1$y))),];
+  fit1 <-remlMM(y ~ (Entry)+(Rep), data1)
+  print(fit1)
+  #Variance component for genotypes###
+  GenoVar=fit1$aov.tab[2,2];
+  GenoVarInd=c(GenoVarInd,GenoVar)
+}
+#V
+V=sum((sqrt(GenoVarInd)-mean(sqrt(GenoVarInd)))^2)/(length(EN)-1)
+V.percent=V/GxE_Var;
+V
+V.percent
+#L
+L=GxE_Var-V;
+L.percent=L/GxE_Var;
+L
+L.percent
+
+#Line/Entry mean heritability
+h2=G_Var/(G_Var+GxE_Var/length(EN)+Error_Var/(length(EN)*2));
+h2
+#Pooled genetic correlation
+rg=G_Var/(G_Var+L)
+rg
+```
+### Step.2 data analysis 
+```R
+env_trait<-env_trait_calculate(data=trait,trait="FTgdd",env="env_code")
+LbyE<-LbyE_calculate(data=trait,trait="FTgdd",env="env_code",line="line_code")
+#envs similarity, plot by ggplot2 like heatmap
+LbyE_corrplot(LbyE=LbyE,cor_type="phetamp",color=c("blue","white","red"))
+
+#Get lines mean and Q25, Q75 within each envs
+etl<-etl_calculate(data=env_info,trait="FTgdd",env="env_code",bycol="lat")
+etl_plotter(data=etl,mean=env_trait) # you could design your plotter use more paras
+
+#Get Best Days window and Env Paras
+Paras <- c('DL', 'GDD', 'PTT', 'PTR', 'PTS');
+#p dap_x dap_y searching_daps according to your data
+pop_cor<-Exhaustive_search(data=env_trait, env_paras=PTT_PTR, searching_daps=80,
+                           p=1, dap_x=80,dap_y=80,LOO=0,Paras=Paras)
+#Result Visualization
+Exhaustive_plotter(data=pop_cor,dap_x=80, dap_y=80,p=1)
+
+#Get Envs mean within env paras
+envMeanPara<-envMeanPara(data=env_trait, env_paras=PTT_PTR, maxR_dap1=18,maxR_dap2=43, Paras=Paras)
+```
+
+### Step.3 CV
+Users can customize the model they need, the function uses the norm reaction by default, the given environment parameters can be obtained from the previous results , fold number represents the number of folds, reshuffle represents the number of repetitions. In RM.G mode, the available models are rrBLUP, LASSO,EN,RR,BA,BB,BC,BL,BRR,RKHS,MKRKHS,SVM,RF and LightGBM.
+```R
+#Check pheno
+pheno<-LbyE[which(as.character(LbyE$line_code)%in%c("line_code",as.character(geno$line_code))),];
+#CV 
+out<-GE_CV(pheno=pheno, geno=geno, env=env_info,
+             para=envMeanPara, Para_Name="PTT", depend="norm",
+             model="rrBLUP", fold=2, reshuffle=5, methods="RM.G")
+#result
+#> mean(out[[3]])
+#[1] 0.8728506
+#> apply(out[[2]],2,mean)
+#     PR12      IA14      PR11      IA13     PR14S      KS11      KS12 
+#0.5418663 0.3868576 0.5381628 0.4759335 0.4871427 0.6219213 0.6380658
+#head(out[[1]])
+#       obs      pre     col para
+#1 1595.988 1588.782 #FF0000 PR12
+#2 1512.918 1576.437 #FF0000 PR12
+```
+
+### Others function Example
+```R
+result<-line_trait_mean(data=trait,trait="FTgdd",mean=env_trait,LbyE=LbyE,row=2)
+MSE<-result[[1]]
+ltm<-result[[2]]
+mse_plotter(MSE)
+
+Reg<-Reg(LbyE=LbyE,env_trait=env_trait)
+Reg_plotter(Reg=Reg)
+Mean_trait_plot(Reg,MSE)
+
+Slope_Intercept<-Slope_Intercept(data=filtered_trait,input=env_trait, env_paras=PTT_PTR,
+                  Para_Name="PTS",line="line_code",trait="PH",filter=5,
+                  maxR_dap1=22, maxR_dap2=35,rounds=4)
+
+prdM <- LOOCV(maxR_dap1=22,maxR_dap2=35,data=filtered_trait,input=env_trait,env_paras=PTT_PTR,
+              Para_Name="PTS",trait="PH",line="line_code",p=1,filter=4)
+
+prdM_plotter(prdM=prdM,data=envMeanPara,trait="PH",Para_Name="PTS");
+envMeanPara_plotter(envMeanPara)
+```
+
+## Documentation
+See full documentation from original repository
+
+## Command line interface
+* env_trait_calculate
+* envMeanPara
+* envMeanPara_plotter
+* etl_calculate
+* etl_plotter
+* Exhaustive_plotter
+* Exhaustive_search
+* GE_CV
+* h2_rrBLUP
+* LbyE_calculate
+* LbyE_corrplot
+* line_trait_mean
+* LOOCV
+* ltm_plotter
+* Mean_trait_plot
+* mse_plotter
+* prdM_plotter
+* Reg
+* Reg_plotter
+* Slope_Intercept
+
+## Implementation notes
+ggGSE is a collection of tools for cross-environmental genome-wide selection prediction that integrates most genome-wide prediction models, both parametric and non-parametric. You can input your own collected data against sample data and get the results you want directly through the built-in functions of the toolkit, which requires no additional statistical knowledge or coding skills and is somewhat user-friendly because it saves users from having to search for various tools and apply them to cross-environmental prediction.
+
+## License
+[GPL-3](https://www.gnu.org/licenses/quick-guide-gplv3.html)
+
+## Reference
+[Li X, Guo T, Wang J, et al. An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops[J]. Molecular Plant](https://www.sciencedirect.com/science/article/pii/S167420522100085X)
+
+[Jarquín D, Crossa J, Lacaze X, et al. A reaction norm model for genomic selection using high-dimensional genomic and environmental data[J]. Theoretical and applied genetics, 2014, 127: 595-607.](https://link.springer.com/article/10.1007/s00122-013-2243-1)
+
+
+
+
+