diff --git a/BayesianMCPMod.Rproj b/BayesianMCPMod.Rproj
index 8e3c2eb..21a4da0 100644
--- a/BayesianMCPMod.Rproj
+++ b/BayesianMCPMod.Rproj
@@ -11,3 +11,7 @@ Encoding: UTF-8
 
 RnwWeave: Sweave
 LaTeX: pdfLaTeX
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source
diff --git a/DESCRIPTION b/DESCRIPTION
index 8744241..8ec6c9b 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: BayesianMCPMod
 Title: Bayesian MCPMod
-Version: 0.1.3-2
+Version: 0.1.3-3
 Authors@R: c(
     person("Sebastian", "Bossert",
            role = "aut",
diff --git a/R/BMCPMod.R b/R/BMCPMod.R
index 0a3f56c..66522e0 100644
--- a/R/BMCPMod.R
+++ b/R/BMCPMod.R
@@ -1,11 +1,13 @@
 #' @title assessDesign
-#' 
-#' @param n_patients tbd
-#' @param mods tbd
-#' @param prior_list tbd
-#' @param n_sim tbd
-#' @param alpha_crit_val tbd
-#' @param simple tbd
+#'.
+#' @description This function performs simulation based trial design evaluations for a set of specified dose-response models
+#'
+#' @param n_patients Vector specifying the planned number of patients per dose group
+#' @param mods An object of class "Mods" as specified in the Dosefinding package.
+#' @param prior_list a prior_list object specifying the utilized prior for the different dose groups 
+#' @param n_sim number of simulations to be performed
+#' @param alpha_crit_val critical value to be used for the testing (on the probability scale)
+#' @param simple boolean variable, defining whether simplified fit will be applied. Passed to the getModelFits function. Default FALSE.
 #' 
 #' @export
 assessDesign <- function (
@@ -65,14 +67,18 @@ assessDesign <- function (
 
 #' @title getContrMat
 #' 
-#' @param mods tbd
-#' @param dose_levels tbd
-#' @param dose_weights tbd
-#' @param prior_list tbd
+#' @description This function calculates contrast vectors that are optimal for detecting certain alternatives. More information and link to publication will be added.
+#' 
+#' @param mods An object of class "Mods" as specified in the Dosefinding package.
+#' @param dose_levels vector containing the different doseage levels.
+#' @param dose_weights Vector specifying weights for the different doses
+#' @param prior_list a prior_list object
+#' 
+#' @return contr_mat Object of class ‘⁠optContr⁠’. A list containing entries contMat and muMat, and CorrMat. Specified in the Dosefinding package.
 #' 
 #' @export
 getContrMat <- function (
-    
+  
   mods,
   dose_levels,
   dose_weights,
@@ -93,10 +99,12 @@ getContrMat <- function (
 
 #' @title getCritProb
 #' 
-#' @param mods tbd
-#' @param dose_levels tbd
-#' @param dose_weights tbd
-#' @param alpha_crit_val tbd
+#' @param mods An object of class "Mods" as specified in the Dosefinding package.
+#' @param dose_levels vector containing the different dosage levels.
+#' @param dose_weights Vector specifying weights for the different doses
+#' @param alpha_crit_val significance level. Default set to 0.025.
+#' 
+#' @return crit_pval multiplicity adjusted critical value on the probability scale.
 #' 
 #' @export
 getCritProb <- function (
@@ -104,7 +112,7 @@ getCritProb <- function (
   mods,
   dose_levels,
   dose_weights,
-  alpha_crit_val
+  alpha_crit_val = 0.025
   
 ) {
   
@@ -125,10 +133,14 @@ getCritProb <- function (
 
 #' @title performBayesianMCPMod
 #' 
-#' @param posteriors_list tbd
-#' @param contr_mat tbd
-#' @param crit_prob tbd
-#' @param simple tbd
+#' @description performs bayesian MCP Test step and modelling.
+#' 
+#' @param posteriors_list a getPosterior object
+#' @param contr_mat a getContrMat object, contrast matrix to be used for the testing step.
+#' @param crit_prob a getCritProb object
+#' @param simple boolean variable, defining whether simplified fit will be applied. Passed to the getModelFits function. Default FALSE.
+#' 
+#' @return bmcpmod test result as well as modelling result.
 #' 
 #' @export
 performBayesianMCPMod <- function (
@@ -206,9 +218,13 @@ addSignificance <- function (
 
 #' @title BayesianMCP
 #' 
-#' @param posteriors_list tbd
-#' @param contr_mat tbd
-#' @param crit_prob tbd
+#' @description performs bayesian MCP Test step.
+#' 
+#' @param posteriors_list a getPosterior object
+#' @param contr_mat a getContrMat object, contrast matrix to be used for the testing step.
+#' @param crit_prob a getCritProb object, specifying the critical value to be used for the testing (on the probability scale)
+#' 
+#' @return b_mcp test result 
 #' 
 #' @export
 performBayesianMCP <- function(
diff --git a/R/modelling.R b/R/modelling.R
index 7895095..1328d24 100644
--- a/R/modelling.R
+++ b/R/modelling.R
@@ -1,9 +1,15 @@
 #' @title getModelFits
 #' 
-#' @param models tbd
-#' @param dose_levels tbd
-#' @param posterior tbd
-#' @param simple tbd
+#' @description Fits dose-response curves for the specified dose-repsonse models, based on the posterior distributions.
+#' For the simplified fit, multivariate normal distributions will be approximated and reduced by one-dimensional normal distributions. 
+#' For the default case, the Nelder-Mead algorithm is used. Will be further updated and links to publication as well as references will be added.
+#' 
+#' @param models list of model names for which a fit will be performed.
+#' @param dose_levels a vector containing the different dosage levels.
+#' @param posterior a getPosterior object, containing the (multivariate) posterior distribution per dosage level. 
+#' @param simple boolean variable, defining whether simplified fit will be applied. Default FALSE.
+#' 
+#' @return model_fits returns a list, containing information about the fitted model coefficients, the prediction per dose group as well as maximum effect and generalized AIC per model.
 #' 
 #' @export
 getModelFits <- function (
diff --git a/R/plot.R b/R/plot.R
index 9f8278b..efd1a4a 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -1,17 +1,19 @@
 #' @title plot.modelFits
-#'
-#' @param x tbd An object of type modelFits
-#' @param gAIC tbd
-#' @param avg_fit tbd
-#' @param cr_intv tbd
-#' @param alpha_CrI tbd
-#' @param cr_bands tbd
-#' @param alpha_CrB tbd
-#' @param n_bs_smpl tbd
-#' @param acc_color tbd
-#' @param ... tbd
 #' 
-#' @return tbd
+#' @description plot function based on the ggplot2 package. Providing visualizations for each model and a average Fit.
+#' More details to be added, as well as references.
+#' @param x an object of type getModelFits
+#' @param gAIC logical value indicating whether gAIC values are shown in the plot. Default TRUE
+#' @param avg_fit logical value indicating whether average fit is presented in the plot. Default TRUE
+#' @param cr_intv logical value indicating whether credible intervals are included in the plot. Default TRUE
+#' @param alpha_CrI numerical value of the width of the credible intervals. Default is set to 0.05 (i.e 95% CI are shown).
+#' @param cr_bands logical value indicating whether bootstrapped based credible bands are shown in the plot. Default FALSE
+#' @param alpha_CrB numerical vector of the width of the credible bands. Default is set to 0.05 and 0.5 (i.e 95% CB and median are shown).
+#' @param n_bs_smpl number of bootstrap samples being used. Default set to 1000.
+#' @param acc_color color of the credible bands. Default set to "orange"
+#' @param ... optional parameter to be passed.
+#' 
+#' @return plts returns a ggplot2 object
 #' @export
 plot.modelFits <- function (
     
diff --git a/R/posterior.R b/R/posterior.R
index df37f5e..5c12d44 100644
--- a/R/posterior.R
+++ b/R/posterior.R
@@ -1,9 +1,14 @@
 #' @title getPriorList
 #' 
-#' @param hist_data tbd
-#' @param dose_levels tbd
-#' @param dose_names prior_list
-#' @param robustify_weight tbd
+#' @param hist_data historical trial summary level data,
+#' needs to be provided as a dataframe. Including information of the
+#' estimates and variability.
+#' @param dose_levels vector of the different doseage levels
+#' @param dose_names character vector of dose levels,
+#' default NULL and will be automatically created
+#' based on the dose levels parameter.
+#' @param robustify_weight Null needs to be provided as a numeric
+#' value for the weight of the robustification component
 #'
 #' @export
 getPriorList <- function (
@@ -11,7 +16,7 @@ getPriorList <- function (
   hist_data,
   dose_levels,
   dose_names       = NULL,
-  robustify_weight = 0.5
+  robustify_weight = NULL
   
 ) {
   
@@ -28,6 +33,10 @@ getPriorList <- function (
   
   prior_ctr <- RBesT::automixfit(gmap)
   
+  if(is.null(robustify_weight) | !is.numeric(robustify_weight)) {
+    stop("robustify_weight needs to be provided and must be numeric")
+  }
+  
   if (!is.null(robustify_weight)) {
     
     prior_ctr <- suppressMessages(RBesT::robustify(
@@ -63,24 +72,32 @@ getPriorList <- function (
 
 #' @title getPosterior
 #' 
-#' @param data tbd
-#' @param prior_list prior_list
-#' @param mu_hat tbd
-#' @param sd_hat tbd
+#' @description Either the patient level data or both the mu_hat as well as the sd_hat must to be provided.
+#' 
+#' @param data dataframe containing the information of dose and response.
+#' Also a simulateData object can be provided.
+#' @param prior_list prior_list object
+#' @param mu_hat vector of estimated mean values
+#' @param sd_hat vector of estimated standard deviations.
 #'
 #' @export
 getPosterior <- function(
-  data,
+  data = NULL,
   prior_list,
   mu_hat = NULL,
   sd_hat = NULL
   
 ) {
-  
+  if (is.null(data)){
+    posterior_list <-getPosteriorI(data_i=NULL, prior_list = prior_list,
+                                   mu_hat     = mu_hat,
+                                   sd_hat     = sd_hat)
+  }else{
   posterior_list <- lapply(split(data, data$simulation), getPosteriorI,
                            prior_list = prior_list,
                            mu_hat     = mu_hat,
                            sd_hat     = sd_hat)
+  }
   
   if (length(posterior_list) == 1) {
     
diff --git a/R/simulation.R b/R/simulation.R
index 7c60082..3eb16ab 100644
--- a/R/simulation.R
+++ b/R/simulation.R
@@ -1,11 +1,18 @@
 #' @title simulateData
 #' 
-#' @param n_patients tbd
-#' @param dose_levels tbd
-#' @param sd tbd
-#' @param mods tbd
-#' @param n_sim tbd
-#' @param true_model tbd
+#' @param n_patients vector containing number of patients as a numerical
+#' value per dose-group.
+#' @param dose_levels vector containing the different doseage levels.
+#' @param sd standard deviation on patient level.
+#' @param mods An object of class "Mods" as specified in the Dosefinding package.
+#' @param n_sim number of simulations to be performed,
+#' Default is 1000
+#' @param true_model Default value is NULL.
+#' Assumed true underlying model. Provided via a String. e.g. "emax".
+#' In case of NULL, all dose-response models, included in the mods input parameter will be used.
+#' 
+#' @return sim_data one list object, containing patient level simulated data for all assumed true models.
+#' Also providing information about simulation iteration, patient number as well as dosage levels.
 #' 
 #' @export
 simulateData <- function(
diff --git a/man/getContrMat.Rd b/man/getContrMat.Rd
index d5393e3..6040050 100644
--- a/man/getContrMat.Rd
+++ b/man/getContrMat.Rd
@@ -7,14 +7,17 @@
 getContrMat(mods, dose_levels, dose_weights, prior_list)
 }
 \arguments{
-\item{mods}{tbd}
+\item{mods}{An object of class "Mods" as specified in the Dosefinding package.}
 
-\item{dose_levels}{tbd}
+\item{dose_levels}{vector containing the different doseage levels.}
 
-\item{dose_weights}{tbd}
+\item{dose_weights}{Vector specifying weights for the different doses}
 
-\item{prior_list}{tbd}
+\item{prior_list}{a prior_list object}
+}
+\value{
+contr_mat Object of class ‘⁠optContr⁠’. A list containing entries contMat and muMat, and CorrMat. Specified in the Dosefinding package.
 }
 \description{
-getContrMat
+This function calculates contrast vectors that are optimal for detecting certain alternatives. More information and link to publication will be added.
 }
diff --git a/man/getCritProb.Rd b/man/getCritProb.Rd
index eeb1b2c..21d3482 100644
--- a/man/getCritProb.Rd
+++ b/man/getCritProb.Rd
@@ -4,16 +4,19 @@
 \alias{getCritProb}
 \title{getCritProb}
 \usage{
-getCritProb(mods, dose_levels, dose_weights, alpha_crit_val)
+getCritProb(mods, dose_levels, dose_weights, alpha_crit_val = 0.025)
 }
 \arguments{
-\item{mods}{tbd}
+\item{mods}{An object of class "Mods" as specified in the Dosefinding package.}
 
-\item{dose_levels}{tbd}
+\item{dose_levels}{vector containing the different doseage levels.}
 
-\item{dose_weights}{tbd}
+\item{dose_weights}{Vector specifying weights for the different doses}
 
-\item{alpha_crit_val}{tbd}
+\item{alpha_crit_val}{significance level. Default set to 0.025.}
+}
+\value{
+crit_pval multiplicity adjusted critical value on the probability scale.
 }
 \description{
 getCritProb
diff --git a/man/getModelFits.Rd b/man/getModelFits.Rd
index f186384..4a87f02 100644
--- a/man/getModelFits.Rd
+++ b/man/getModelFits.Rd
@@ -7,14 +7,19 @@
 getModelFits(models, dose_levels, posterior, simple = FALSE)
 }
 \arguments{
-\item{models}{tbd}
+\item{models}{list of model names for which a fit will be performed.}
 
-\item{dose_levels}{tbd}
+\item{dose_levels}{a vector containing the different dosage levels.}
 
-\item{posterior}{tbd}
+\item{posterior}{a getPosterior object, containing the (multivariate) posterior distribution per dosage level.}
 
-\item{simple}{tbd}
+\item{simple}{boolean variable, defining whether simplified fit will be applied. Default FALSE.}
+}
+\value{
+model_fits returns a list, containing information about the fitted model coefficients, the prediction per dose group as well as maximum effect and generalized AIC per model.
 }
 \description{
-getModelFits
+Fits dose-response curves for the specified dose-repsonse models, based on the posterior distributions.
+For the simplified fit, multivariate normal distributions will be approximated and reduced by one-dimensional normal distributions.
+For the default case, the Nelder-Mead algorithm is used. Will be further updated and links to publication as well as references will be added.
 }
diff --git a/man/getPosterior.Rd b/man/getPosterior.Rd
index 501f5de..22ac81f 100644
--- a/man/getPosterior.Rd
+++ b/man/getPosterior.Rd
@@ -7,14 +7,15 @@
 getPosterior(data, prior_list, mu_hat = NULL, sd_hat = NULL)
 }
 \arguments{
-\item{data}{tbd}
+\item{data}{dataframe containing the information of dose and response.
+Also a simulateData object can be provided.}
 
-\item{prior_list}{prior_list}
+\item{prior_list}{prior_list object}
 
-\item{mu_hat}{tbd}
+\item{mu_hat}{vector of estimated mean values}
 
-\item{sd_hat}{tbd}
+\item{sd_hat}{vector of estimated standard deviations.}
 }
 \description{
-getPosterior
+Either the patient level data or both the mu_hat as well as the sd_hat must to be provided.
 }
diff --git a/man/getPriorList.Rd b/man/getPriorList.Rd
index bb7e346..7fe0937 100644
--- a/man/getPriorList.Rd
+++ b/man/getPriorList.Rd
@@ -4,16 +4,26 @@
 \alias{getPriorList}
 \title{getPriorList}
 \usage{
-getPriorList(hist_data, dose_levels, dose_names = NULL, robustify_weight = 0.5)
+getPriorList(
+  hist_data,
+  dose_levels,
+  dose_names = NULL,
+  robustify_weight = NULL
+)
 }
 \arguments{
-\item{hist_data}{tbd}
+\item{hist_data}{historical trial summary level data,
+needs to be provided as a dataframe. Including information of the
+estimates and variability.}
 
-\item{dose_levels}{tbd}
+\item{dose_levels}{vector of the different doseage levels}
 
-\item{dose_names}{prior_list}
+\item{dose_names}{character vector of dose levels,
+default NULL and will be automatically created
+based on the dose levels parameter.}
 
-\item{robustify_weight}{tbd}
+\item{robustify_weight}{Null needs to be provided as a numeric
+value for the weight of the robustification component}
 }
 \description{
 getPriorList
diff --git a/man/performBayesianMCP.Rd b/man/performBayesianMCP.Rd
index e6b3543..66e13e5 100644
--- a/man/performBayesianMCP.Rd
+++ b/man/performBayesianMCP.Rd
@@ -7,12 +7,15 @@
 performBayesianMCP(posteriors_list, contr_mat, crit_prob)
 }
 \arguments{
-\item{posteriors_list}{tbd}
+\item{posteriors_list}{a getPosterior object}
 
-\item{contr_mat}{tbd}
+\item{contr_mat}{a getContrMat object, contrast matrix to be used for the testing step.}
 
-\item{crit_prob}{tbd}
+\item{crit_prob}{a getCritProb object}
+}
+\value{
+b_mcp test result
 }
 \description{
-BayesianMCP
+performs bayesian MCP Test step.
 }
diff --git a/man/performBayesianMCPMod.Rd b/man/performBayesianMCPMod.Rd
index 83a4a06..1bc33dc 100644
--- a/man/performBayesianMCPMod.Rd
+++ b/man/performBayesianMCPMod.Rd
@@ -7,14 +7,17 @@
 performBayesianMCPMod(posteriors_list, contr_mat, crit_prob, simple = FALSE)
 }
 \arguments{
-\item{posteriors_list}{tbd}
+\item{posteriors_list}{a getPosterior object}
 
-\item{contr_mat}{tbd}
+\item{contr_mat}{a getContrMat object, contrast matrix to be used for the testing step.}
 
-\item{crit_prob}{tbd}
+\item{crit_prob}{a getCritProb object}
 
-\item{simple}{tbd}
+\item{simple}{boolean variable, defining whether simplified fit will be applied. Passed to the getModelFits function. Default FALSE.}
+}
+\value{
+bmcpmod test result as well as modelling result.
 }
 \description{
-performBayesianMCPMod
+performs bayesian MCP Test step and modelling.
 }
diff --git a/man/plot.modelFits.Rd b/man/plot.modelFits.Rd
index 3b35078..0cb1c26 100644
--- a/man/plot.modelFits.Rd
+++ b/man/plot.modelFits.Rd
@@ -18,29 +18,30 @@
 )
 }
 \arguments{
-\item{x}{tbd An object of type modelFits}
+\item{x}{an object of type getModelFits}
 
-\item{gAIC}{tbd}
+\item{gAIC}{logical value indicating whether gAIC values are shown in the plot. Default TRUE}
 
-\item{avg_fit}{tbd}
+\item{avg_fit}{logical value indicating whether average fit is presented in the plot. Default TRUE}
 
-\item{cr_intv}{tbd}
+\item{cr_intv}{logical value indicating whether credible intervals are included in the plot. Default TRUE}
 
-\item{alpha_CrI}{tbd}
+\item{alpha_CrI}{numerical value of the width of the credible intervals. Default is set to 0.05 (i.e 95\% CI are shown).}
 
-\item{cr_bands}{tbd}
+\item{cr_bands}{logical value indicating whether bootstrapped based credible bands are shown in the plot. Default FALSE}
 
-\item{alpha_CrB}{tbd}
+\item{alpha_CrB}{numerical vector of the width of the credible bands. Default is set to 0.05 and 0.5 (i.e 95\% CB and median are shown).}
 
-\item{n_bs_smpl}{tbd}
+\item{n_bs_smpl}{number of bootstrap samples being used. Default set to 1000.}
 
-\item{acc_color}{tbd}
+\item{acc_color}{color of the credible bands. Default set to "orange"}
 
-\item{...}{tbd}
+\item{...}{optional parameter to be passed.}
 }
 \value{
-tbd
+plts returns a ggplot2 object
 }
 \description{
-plot.modelFits
+plot function based on the ggplot2 package. Providing visualizations for each model and a average Fit.
+More details to be added, as well as references.
 }
diff --git a/man/simulateData.Rd b/man/simulateData.Rd
index 32038df..9afd8da 100644
--- a/man/simulateData.Rd
+++ b/man/simulateData.Rd
@@ -14,17 +14,25 @@ simulateData(
 )
 }
 \arguments{
-\item{n_patients}{tbd}
+\item{n_patients}{vector containing number of patients as a numerical
+value per dose-group.}
 
-\item{dose_levels}{tbd}
+\item{dose_levels}{vector containing the different doseage levels.}
 
-\item{sd}{tbd}
+\item{sd}{standard deviation on patient level.}
 
-\item{mods}{tbd}
+\item{mods}{An object of class "Mods" as specified in the Dosefinding package.}
 
-\item{n_sim}{tbd}
+\item{n_sim}{number of simulations to be performed,
+Default is 1000}
 
-\item{true_model}{tbd}
+\item{true_model}{Default value is NULL.
+Assumed true underlying model. Provided via a String. e.g. "emax".
+In case of NULL, all dose-response models, included in the mods input parameter will be used.}
+}
+\value{
+sim_data one list object, containing patient level simulated data for all assumed true models.
+Also providing information about simulation iteration, patient number as well as dosage levels.
 }
 \description{
 simulateData
diff --git a/vignettes/Simulation_Example.Rmd b/vignettes/Simulation_Example.Rmd
index bcd8cef..8e43208 100644
--- a/vignettes/Simulation_Example.Rmd
+++ b/vignettes/Simulation_Example.Rmd
@@ -52,7 +52,8 @@ dose_levels <- c(0, 2.5, 5, 10, 20)
 
 prior_list <- getPriorList(
   hist_data   = hist_data,
-  dose_levels = dose_levels)
+  dose_levels = dose_levels,
+  robustify_weight = 0.5)
 
 ```
 
diff --git a/vignettes/analysis_normal.Rmd b/vignettes/analysis_normal.Rmd
index e84ebac..8420240 100644
--- a/vignettes/analysis_normal.Rmd
+++ b/vignettes/analysis_normal.Rmd
@@ -52,7 +52,7 @@ dose_levels <- c(0, 2.5, 5, 10)
 
 prior_list <- getPriorList(
   hist_data   = hist_data,
-  dose_levels = dose_levels)
+  dose_levels = dose_levels,robustify_weight = 0.5)
 
 ```
 
@@ -92,16 +92,7 @@ n_patients <- c(150, 142, 147, 149)
 As outlined in citePaper, in a first step the posterior is calculated combining the prior information with the estimated results of the new trial.
 
 ```{r Trial results}
-#Simulation of new trial 
-##Note: This step should not be required, as we provide summary measures directly from the new trial
-data_emax <- simulateData(
-  n_patients  = n_patients,
-  dose_levels = dose_levels,
-  sd          = attr(prior_list, "sd_tot"),
-  mods        = mods,
-  true_model  = "emax")
-
-posterior <- getPosterior(prior=prior_list,data=data_emax,
+posterior <- getPosterior(prior=prior_list,
                                mu_hat = new_trial$rslt,
                                sd_hat = new_trial$se)
 
@@ -141,7 +132,7 @@ BMCP_result <- performBayesianMCP(
 
 #BMCP_result2
 
-BMCP_result
+BMCP_result[1,1:5]
 ```
 The testing step is significant indicating a non-flat dose-response shape. In detail the p-values for the emax model is the most significant one.