diff --git a/DESCRIPTION b/DESCRIPTION
index 2c97d4f..7ccbd7f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -26,7 +26,8 @@ Imports:
     future.apply,
     data.table,
     expm,
-    gridBase
+    gridBase,
+    dplyr
 Suggests:
     knitr,
     rmarkdown,
diff --git a/R/boruta.R b/R/boruta.R
index 0ba714b..ec87a56 100644
--- a/R/boruta.R
+++ b/R/boruta.R
@@ -27,7 +27,6 @@
 #' 
 #' @keywords tree models multivariate
 #' @export
-
 boruta <- function(model,
                    data,
                    control = NULL,
@@ -38,63 +37,21 @@ boruta <- function(model,
                    verbose=FALSE,
                    ...) {
   
-  # initial checks
-  stopifnot(percentile_threshold>=0)
-  stopifnot(percentile_threshold<=1)
-  stopifnot(is.numeric(rounds))
-  stopifnot(rounds>0)
-  
-  preds_important <- c()
-  preds_unimportant <- c()
-
-  cur_round = 1
-  temp_vims <- list()
-   
-  while(cur_round <= rounds) {
-  vim_boruta <- .boruta(model=model,
-          data=data,
-          control=control,
-          predictors=predictors,
-          percentile_threshold = percentile_threshold,
-          ...) 
-  browser()
-  # add predictors to list of unimportant variables
-  preds_unimportant <- c(preds_unimportant, names(vim_boruta$filter)[!vim_boruta$filter])
-  # remove them from the dataset
-  data <- data[, -c(preds_unimportant)]
-  temp_vims[[cur_round]] <-vim_boruta
-  }
-  
-  result <- list(
-    preds_unimportant,
-    rounds = rounds
-  )
   
-  return(result)
-}
-
-.boruta <- function(model,
-                   data,
-                   control = NULL,
-                   predictors = NULL,
-                   percentile_threshold = 1,
-                   num_shadows = 1,
-                   ...) {
-  
-  # make sure that no column names start with "shadow_" prefix
-  stopifnot(all(sapply(names(data), function(x) {!startsWith(x, "shadow_")})))
-
   # detect model (warning: duplicated code)
-  if (inherits(model, "MxModel") || inherits(model, "MxRAMModel")) {
-    tmp <- getPredictorsOpenMx(mxmodel = model, dataset = data, covariates = predictors)
-
-  } else if (inherits(model,"lavaan")){
+  if (inherits(model,"MxModel") || inherits(model,"MxRAMModel")) {
+    tmp <- getPredictorsOpenMx(mxmodel=model, dataset=data, covariates=predictors)
+    model.ids <- tmp[[1]]
+    covariate.ids <- tmp[[2]]
+    #  } else if (inherits(model,"lavaan")){
     
-    tmp <- getPredictorsLavaan(model, data, predictors)
+    # } else if ((inherits(model,"ctsemFit")) || (inherits(model,"ctsemInit"))) {
+    #
   } else {
-    ui_stop("Unknown model type selected. Use OpenMx or lavaanified lavaan models!")
+    ui_stop("Unknown model type selected. Use OpenMx or lavaanified lavaan models!");
   }
-
+  
+  # initial checks
   # Checks on x & y from the boruta package
   if(length(grep('^shadow',covariate.ids)>0)) 
     stop('Attributes with names starting from "shadow" are reserved for internal use. Please rename them.')
@@ -114,7 +71,7 @@ boruta <- function(model,
   names(impHistory) <- c(predictors, "shadowMin", "shadowMean", "shadowMax")
   decisionList <- data.frame(predictor=predictors, decision = "Tentative",
                              hitCount = 0, raw.p=NA, adjusted.p=NA)
-
+  
   # TODO: Parallelize the first five runs.
   for(runNo in 1:maxRuns) {
     if(verbose) {
@@ -143,10 +100,10 @@ boruta <- function(model,
     
     # run the forest
     forest <- semforest(model, current.data, control, current.predictors, ...)
-  
+    
     # run variable importance
     vim <- varimp(forest)
-  
+    
     # get variable importance from shadow features
     shadow_names <- names(current.data)[shadow.ids]
     agvim <- aggregateVarimp(vim, aggregate="mean")
@@ -195,12 +152,43 @@ boruta <- function(model,
   vim$impHistory <- impHistory
   vim$decisions <- decisionList$decision
   vim$details <- decisionList
-
+  
   vim$filter <- decisionList$decision == "Confirmed"  # Turns into hitreg
   vim$boruta <- TRUE
   
+  class(vim) <- "boruta"
+  
   # TODO: Loop ends here with some reporting.
   
   return(vim)
 }
 
+#' @exportS3Method plot boruta
+plot.boruta = function(vim, type=0, ...) {
+  decisionList = vim$details
+  impHistory = vim$impHistory
+  impHistory <- impHistory |>
+    dplyr::mutate(rnd=1:nrow(impHistory)) |>
+    tidyr::pivot_longer(cols = -last_col()) |> #everything()) |> 
+    dplyr::left_join(data.frame(decisionList), by=join_by("name"=="predictor")) |>
+    dplyr::mutate(decision = case_when(is.na(decision)~"Shadow", .default=decision)) |>
+    dplyr::group_by(name) |> mutate(median_value = median(value,na.rm=TRUE))
+ 
+  if (type==0) {
+   ggplot2::ggplot(impHistory, 
+                  aes(x=stats::reorder(name, median_value), 
+                      y=value, color=decision)) + 
+    ggplot2::geom_boxplot()+
+    ggplot2::xlab("")+
+    ggplot2::ylab("Importance")+
+    scale_color_discrete(name = "Predictor")+
+    theme(axis.text.x = element_text(angle = 45, hjust = 1))
+  } else if (type==1) {
+    ggplot2::ggplot(impHistory,
+                    aes(x=rnd, y=value,group=name,col=name))+geom_line()+ geom_hline(aes(yintercept=median_value,col=name),lwd=2)+
+      xlab("Round")+ylab("Importance")+scale_color_discrete(name = "Predictor")
+  } else {
+    stop("Unknown graph type. Please choose 0 or 1.")
+  }
+  
+}
\ No newline at end of file
diff --git a/R/toTable.R b/R/toTable.R
index 337c235..2be2bf7 100644
--- a/R/toTable.R
+++ b/R/toTable.R
@@ -66,7 +66,6 @@ alls <- unique(alls)
 
 
 # create table
-#covariate.names <-simplify2array(tree$result$btn.matrix[2,])
 covariate.names <- getCovariatesFromTree(tree)
 
 # default is to display all parameters
@@ -79,10 +78,12 @@ all.names <- c(covariate.names, added.param.cols)
 
 str.matrix <- matrix(NA, nrow = length(rowdata),ncol=length(all.names))
 
+# convert to a data frame to avoid coercion to string
+str.matrix <- data.frame(str.matrix)
+
 colnames(str.matrix) <- all.names
 
 for (i in 1:length(rowdata)) {
-#  result.row <- rep(" ",length(covariate.names))
   myrow <- rowdata[[i]][[1]]
   for (j in 1:length(myrow)) {
     myitem <- myrow[[j]]
@@ -126,7 +127,6 @@ for (i in 1:length(rowdata)) {
   }
   }
   
-#  result.string <- paste(result.string,paste(result.row,collapse="\t"),"\n")
 }
 
 ## prune empty columns?
@@ -139,7 +139,7 @@ if (length(is.col.empty)>0) {
 sortby <- apply(str.matrix,2,function(x){sum(!is.na(x))})
 if (!is.null(added.param.cols)) {
  remids <- (dim(str.matrix)[2]-length(added.param.cols)+1):(dim(str.matrix)[2])
- sortby[remids] <- sortby[remids]-999999
+ sortby[remids] <- sortby[remids]-999999 # bad style ^^ #TODO
 }
 sort.ix <- sort(sortby,index.return=TRUE,decreasing = TRUE)$ix
 str.matrix <- str.matrix[, sort.ix]
@@ -156,7 +156,6 @@ str.matrix[is.na(str.matrix)]<-""
 ## and display
 #cat(result.string)
 
-#require("openxlsx")
 
 return(str.matrix)
 
diff --git a/man/boruta.Rd b/man/boruta.Rd
index 4921d25..6a8cc8a 100644
--- a/man/boruta.Rd
+++ b/man/boruta.Rd
@@ -6,7 +6,17 @@
 \alias{print.boruta}
 \title{Run the Boruta algorithm on a sem tree}
 \usage{
-boruta(model, data, control = NULL, predictors = NULL, ...)
+boruta(
+  model,
+  data,
+  control = NULL,
+  predictors = NULL,
+  maxRuns = 30,
+  pAdjMethod = "none",
+  alpha = 0.05,
+  verbose = FALSE,
+  ...
+)
 }
 \arguments{
 \item{model}{A template SEM. Same as in \code{semtree}.}
@@ -17,12 +27,25 @@ boruta(model, data, control = NULL, predictors = NULL, ...)
 
 \item{predictors}{An optional list of covariates. See semtree code example.}
 
-\item{\dots}{Optional parameters.}
+\item{maxRuns}{Maximum number of boruta search cycles}
 
-\item{constraints}{An optional list of covariates. See semtree code example.}
+\item{pAdjMethod}{A value from \link{stats::p.adjust.methods} defining a 
+multiple testing correction method}
+
+\item{alpha}{p-value cutoff for decisionmaking. Default .05}
+
+\item{verbose}{Verbosity level for boruta processing 
+similar to the same argument in \link{semtree.control} and 
+\link{semforest.control}}
+
+\item{\dots}{Optional parameters to undefined subfunctions}
 }
 \value{
-A boruta object.
+A vim object with several elements that need work. 
+         Of particular note, `$importance` carries mean importance; 
+         `$decision` denotes Accepted/Rejected/Tentative;
+         `$impHistory` has the entire varimp history; and
+         `$details` has exit values for each parameter.
 }
 \description{
 Grows a series of SEM Forests following the boruta algorithm to determine
diff --git a/man/semtree.control.Rd b/man/semtree.control.Rd
index 7ce8847..df9d886 100644
--- a/man/semtree.control.Rd
+++ b/man/semtree.control.Rd
@@ -8,7 +8,7 @@
 \usage{
 semtree.control(
   method = c("naive", "score", "fair", "fair3"),
-  min.N = 20,
+  min.N = NULL,
   max.depth = NA,
   alpha = 0.05,
   alpha.invariance = NA,
@@ -24,7 +24,7 @@ semtree.control(
   report.level = 0,
   exclude.code = NA,
   linear = TRUE,
-  min.bucket = 10,
+  min.bucket = NULL,
   naive.bonferroni.type = 0,
   missing = "ignore",
   use.maxlm = FALSE,