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diff --git a/ehdenRaPrediction/data/Validation/PanTher/Analysis_69/validationResult.rds b/ehdenRaPrediction/data/Validation/PanTher/Analysis_69/validationResult.rds
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diff --git a/ehdenRaPrediction/data/settings.csv b/ehdenRaPrediction/data/settings.csv
new file mode 100644
index 00000000..2ba48300
--- /dev/null
+++ b/ehdenRaPrediction/data/settings.csv
@@ -0,0 +1,21 @@
+"outcomeId","cohortId","modelSettingsId","analysisId","devDatabase","populationSettingId","modelSettingId","covariateSettingId","modelSettingName","addExposureDaysToStart","riskWindowStart","addExposureDaysToEnd","riskWindowEnd","plpDataFolder","studyPopFile","plpResultFolder","cohortName","outcomeName"
+183,257,2,37,"dod",1,1,2,"Lasso Logistic Regression",0,1,0,730,"./EHDENRAPredictionResults/PlpData_L2_T257","./EHDENRAPredictionResults/StudyPop_L1_T257_O183.rds","./EHDENRAPredictionResults/Analysis_37","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Stroke (ischemic or hemorrhagic) events (any visit)"
+183,257,1,15,"dod",1,1,1,"Lasso Logistic Regression",0,1,0,730,"./EHDENRAPredictionResults/PlpData_L1_T257","./EHDENRAPredictionResults/StudyPop_L1_T257_O183.rds","./EHDENRAPredictionResults/Analysis_15","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Stroke (ischemic or hemorrhagic) events (any visit)"
+185,257,2,35,"dod",1,1,2,"Lasso Logistic Regression",0,1,0,730,"./EHDENRAPredictionResults/PlpData_L2_T257","./EHDENRAPredictionResults/StudyPop_L1_T257_O185.rds","./EHDENRAPredictionResults/Analysis_35","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Acute myocardial infarction events (in any visit)"
+185,257,1,13,"dod",1,1,1,"Lasso Logistic Regression",0,1,0,730,"./EHDENRAPredictionResults/PlpData_L1_T257","./EHDENRAPredictionResults/StudyPop_L1_T257_O185.rds","./EHDENRAPredictionResults/Analysis_13","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Acute myocardial infarction events (in any visit)"
+193,257,4,85,"dod",2,1,2,"Lasso Logistic Regression",0,1,0,90,"./EHDENRAPredictionResults/PlpData_L2_T257","./EHDENRAPredictionResults/StudyPop_L2_T257_O193.rds","./EHDENRAPredictionResults/Analysis_85","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Pancytopenia events using diagnoses and measurements"
+193,257,3,63,"dod",2,1,1,"Lasso Logistic Regression",0,1,0,90,"./EHDENRAPredictionResults/PlpData_L1_T257","./EHDENRAPredictionResults/StudyPop_L2_T257_O193.rds","./EHDENRAPredictionResults/Analysis_63","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Pancytopenia events using diagnoses and measurements"
+197,257,3,49,"dod",2,1,1,"Lasso Logistic Regression",0,1,0,90,"./EHDENRAPredictionResults/PlpData_L1_T257","./EHDENRAPredictionResults/StudyPop_L2_T257_O197.rds","./EHDENRAPredictionResults/Analysis_49","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Opportunistic Infections"
+197,257,4,71,"dod",2,1,2,"Lasso Logistic Regression",0,1,0,90,"./EHDENRAPredictionResults/PlpData_L2_T257","./EHDENRAPredictionResults/StudyPop_L2_T257_O197.rds","./EHDENRAPredictionResults/Analysis_71","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Opportunistic Infections"
+206,258,5,96,"dod",3,1,1,"Lasso Logistic Regression",0,365,0,1826,"./EHDENRAPredictionResults/PlpData_L1_T258","./EHDENRAPredictionResults/StudyPop_L3_T258_O206.rds","./EHDENRAPredictionResults/Analysis_96","[EHDEN RA] Female new users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Persons with a Malignant neoplasm of breast 1 dx"
+206,258,6,118,"dod",3,1,2,"Lasso Logistic Regression",0,365,0,1826,"./EHDENRAPredictionResults/PlpData_L2_T258","./EHDENRAPredictionResults/StudyPop_L3_T258_O206.rds","./EHDENRAPredictionResults/Analysis_118","[EHDEN RA] Female new users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Persons with a Malignant neoplasm of breast 1 dx"
+208,258,6,120,"dod",3,1,2,"Lasso Logistic Regression",0,365,0,1826,"./EHDENRAPredictionResults/PlpData_L2_T258","./EHDENRAPredictionResults/StudyPop_L3_T258_O208.rds","./EHDENRAPredictionResults/Analysis_120","[EHDEN RA] Female new users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Persons with a Malignant neoplasm of uterus 1 dx"
+208,258,5,98,"dod",3,1,1,"Lasso Logistic Regression",0,365,0,1826,"./EHDENRAPredictionResults/PlpData_L1_T258","./EHDENRAPredictionResults/StudyPop_L3_T258_O208.rds","./EHDENRAPredictionResults/Analysis_98","[EHDEN RA] Female new users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Persons with a Malignant neoplasm of uterus 1 dx"
+218,257,5,99,"dod",3,1,1,"Lasso Logistic Regression",0,365,0,1826,"./EHDENRAPredictionResults/PlpData_L1_T257","./EHDENRAPredictionResults/StudyPop_L3_T257_O218.rds","./EHDENRAPredictionResults/Analysis_99","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Persons with a Malignant neoplasm of colon and rectum 1 dx"
+218,257,6,121,"dod",3,1,2,"Lasso Logistic Regression",0,365,0,1826,"./EHDENRAPredictionResults/PlpData_L2_T257","./EHDENRAPredictionResults/StudyPop_L3_T257_O218.rds","./EHDENRAPredictionResults/Analysis_121","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Persons with a Malignant neoplasm of colon and rectum 1 dx"
+253,257,4,67,"dod",2,1,2,"Lasso Logistic Regression",0,1,0,90,"./EHDENRAPredictionResults/PlpData_L2_T257","./EHDENRAPredictionResults/StudyPop_L2_T257_O253.rds","./EHDENRAPredictionResults/Analysis_67","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Serious Infection, opportunistic infections and other infections of interest event"
+253,257,3,45,"dod",2,1,1,"Lasso Logistic Regression",0,1,0,90,"./EHDENRAPredictionResults/PlpData_L1_T257","./EHDENRAPredictionResults/StudyPop_L2_T257_O253.rds","./EHDENRAPredictionResults/Analysis_45","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Serious Infection, opportunistic infections and other infections of interest event"
+259,257,4,87,"dod",2,1,2,"Lasso Logistic Regression",0,1,0,90,"./EHDENRAPredictionResults/PlpData_L2_T257","./EHDENRAPredictionResults/StudyPop_L2_T257_O259.rds","./EHDENRAPredictionResults/Analysis_87","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Leukopenia events using diagnoses and measurements"
+259,257,3,65,"dod",2,1,1,"Lasso Logistic Regression",0,1,0,90,"./EHDENRAPredictionResults/PlpData_L1_T257","./EHDENRAPredictionResults/StudyPop_L2_T257_O259.rds","./EHDENRAPredictionResults/Analysis_65","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Leukopenia events using diagnoses and measurements"
+260,257,4,83,"dod",2,1,2,"Lasso Logistic Regression",0,1,0,90,"./EHDENRAPredictionResults/PlpData_L2_T257","./EHDENRAPredictionResults/StudyPop_L2_T257_O260.rds","./EHDENRAPredictionResults/Analysis_83","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Pancytopenia or leukopenia events using diagnoses and measurements"
+260,257,3,61,"dod",2,1,1,"Lasso Logistic Regression",0,1,0,90,"./EHDENRAPredictionResults/PlpData_L1_T257","./EHDENRAPredictionResults/StudyPop_L2_T257_O260.rds","./EHDENRAPredictionResults/Analysis_61","[EHDEN RA] New users of methoxtrexate monotherapy used for PLP","[EHDEN RA] Pancytopenia or leukopenia events using diagnoses and measurements"
diff --git a/ehdenRaPrediction/global.R b/ehdenRaPrediction/global.R
new file mode 100644
index 00000000..d7f2c630
--- /dev/null
+++ b/ehdenRaPrediction/global.R
@@ -0,0 +1,14 @@
+# uncomment if running standalone
+##runPlp <- readRDS(file.path("data","results.rds"))
+##validatePlp <- readRDS(file.path("data","extValidation.rds"))
+source("utils.R")
+analysesLocation <- file.path("data")
+allPerformance <- summaryPlpAnalyses(analysesLocation)
+plpResultLocation <- allPerformance[,c('plpResultLocation', 'plpResultLoad')]
+#allPerformance$combinedModelSettingName <- paste0(allPerformance$modelSettingName,'-', allPerformance$modelSettingsId
+formatPerformance <- allPerformance[,c('analysisId','devDatabase','valDatabase','cohortName','outcomeName','modelSettingName','riskWindowStart', 'riskWindowEnd', 'AUC','AUPRC', 'populationSize','outcomeCount','incidence',
+                                       'addExposureDaysToStart','addExposureDaysToEnd')]
+colnames(formatPerformance) <- c('Analysis','Dev', 'Val', 'T', 'O','Model', 'TAR start', 'TAR end', 'AUC','AUPRC', 'T Size','O Count','O Incidence (%)', 'addExposureDaysToStart','addExposureDaysToEnd')
+
+
+
diff --git a/ehdenRaPrediction/plots.R b/ehdenRaPrediction/plots.R
new file mode 100644
index 00000000..4a848eed
--- /dev/null
+++ b/ehdenRaPrediction/plots.R
@@ -0,0 +1,480 @@
+#============  DYNAMIC PLOTS ======================
+#++++++++++++++++++++++++++++++++++++++++++++++++++
+
+plotShiny <- function(eval, pointOfInterest){
+  
+  data <- eval$thresholdSummary[eval$thresholdSummary$Eval%in%c('test','validation'),]
+  # pointOfInterest # this is a threshold
+  pointOfInterest <- data[pointOfInterest,]
+  rocobject <- plotly::plot_ly(x = 1-c(0,data$specificity,1)) %>%
+    plotly::add_lines(y = c(1,data$sensitivity,0),name = "hv", 
+                      text = paste('Risk Threshold:',c(0,data$predictionThreshold,1)),
+                      line = list(shape = "hv",
+                                  color = 'rgb(22, 96, 167)'),
+                      fill = 'tozeroy') %>%
+    plotly::add_trace(x= c(0,1), y = c(0,1),mode = 'lines',
+                      line = list(dash = "dash"), color = I('black'),
+                      type='scatter') %>%
+    plotly::add_trace(x= 1-pointOfInterest$specificity, y=pointOfInterest$sensitivity, 
+                      mode = 'markers', symbols='x') %>%  # change the colour of this!
+    plotly::add_lines(x=c(1-pointOfInterest$specificity, 1-pointOfInterest$specificity),
+                      y = c(0,1),
+                      line = list(dash ='solid',
+                                  color = 'black')) %>%
+    plotly::layout(title = "ROC Plot",
+                   xaxis = list(title = "1-specificity"),
+                   yaxis = list (title = "Sensitivity"),
+                   showlegend = FALSE)
+  
+  popAv <- data$trueCount[1]/(data$trueCount[1] + data$falseCount[1])
+  probject <- plotly::plot_ly(x = data$sensitivity) %>%
+    plotly::add_lines(y = data$positivePredictiveValue, name = "hv", 
+                      text = paste('Risk Threshold:',data$predictionThreshold),
+                      line = list(shape = "hv",
+                                  color = 'rgb(22, 96, 167)'),
+                      fill = 'tozeroy') %>%
+    plotly::add_trace(x= c(0,1), y = c(popAv,popAv),mode = 'lines',
+                      line = list(dash = "dash"), color = I('red'),
+                      type='scatter') %>%
+    plotly::add_trace(x= pointOfInterest$sensitivity, y=pointOfInterest$positivePredictiveValue, 
+                      mode = 'markers', symbols='x') %>%  
+    plotly::add_lines(x=c(pointOfInterest$sensitivity, pointOfInterest$sensitivity),
+                      y = c(0,1),
+                      line = list(dash ='solid',
+                                  color = 'black')) %>%
+    plotly::layout(title = "PR Plot",
+                   xaxis = list(title = "Recall"),
+                   yaxis = list (title = "Precision"),
+                   showlegend = FALSE)
+  
+  # add F1 score
+  f1object <- plotly::plot_ly(x = data$predictionThreshold) %>%
+    plotly::add_lines(y = data$f1Score, name = "hv", 
+                      text = paste('Risk Threshold:',data$predictionThreshold),
+                      line = list(shape = "hv",
+                                  color = 'rgb(22, 96, 167)'),
+                      fill = 'tozeroy') %>%
+    plotly::add_trace(x= pointOfInterest$predictionThreshold, y=pointOfInterest$f1Score, 
+                      mode = 'markers', symbols='x') %>%  
+    plotly::add_lines(x=c(pointOfInterest$predictionThreshold, pointOfInterest$predictionThreshold),
+                      y = c(0,1),
+                      line = list(dash ='solid',
+                                  color = 'black')) %>%
+    plotly::layout(title = "F1-Score Plot",
+                   xaxis = list(title = "Prediction Threshold"),
+                   yaxis = list (title = "F1-Score"),
+                   showlegend = FALSE)
+  # create 2x2 table with TP, FP, TN, FN and threshold
+  threshold <- pointOfInterest$predictionThreshold
+  TP <- pointOfInterest$truePositiveCount
+  TN <- pointOfInterest$trueNegativeCount
+  FP <- pointOfInterest$falsePositiveCount
+  FN <- pointOfInterest$falseNegativeCount
+  preferenceThreshold <- pointOfInterest$preferenceThreshold
+  
+  return(list(roc = rocobject,
+              pr = probject,
+              f1score = f1object,
+              threshold = threshold, prefthreshold=preferenceThreshold,
+              TP = TP, TN=TN,
+              FP= FP, FN=FN))
+}
+
+plotCovariateSummary <- function(covariateSummary){
+  
+  #writeLines(paste(colnames(covariateSummary)))
+  #writeLines(paste(covariateSummary[1,]))
+  # remove na values 
+  covariateSummary$CovariateMeanWithNoOutcome[is.na(covariateSummary$CovariateMeanWithNoOutcome)] <- 0
+  covariateSummary$CovariateMeanWithOutcome[is.na(covariateSummary$CovariateMeanWithOutcome)] <- 0
+  if(!'covariateValue'%in%colnames(covariateSummary)){
+    covariateSummary$covariateValue <- 1
+  }
+  if(sum(is.na(covariateSummary$covariateValue))>0){
+    covariateSummary$covariateValue[is.na(covariateSummary$covariateValue)] <- 0
+  }
+  
+  # SPEED EDIT remove the none model variables
+  covariateSummary <- covariateSummary[covariateSummary$covariateValue!=0,]
+  
+  # save dots based on coef value 
+  covariateSummary$size <- abs(covariateSummary$covariateValue)
+  covariateSummary$size[is.na(covariateSummary$size)] <- 4
+  covariateSummary$size <- 4+4*covariateSummary$size/max(covariateSummary$size)
+  
+  # color based on analysis id
+  covariateSummary$color <- sapply(covariateSummary$covariateName, function(x) ifelse(is.na(x), '', strsplit(as.character(x), ' ')[[1]][1]))
+  
+  l <- list(x = 0.01, y = 1,
+            font = list(
+              family = "sans-serif",
+              size = 10,
+              color = "#000"),
+            bgcolor = "#E2E2E2",
+            bordercolor = "#FFFFFF",
+            borderwidth = 1)
+  
+  #covariateSummary$annotation <- sapply(covariateSummary$covariateName, getName)
+  covariateSummary$annotation <- covariateSummary$covariateName
+  
+  
+  ind <- covariateSummary$CovariateMeanWithNoOutcome <=1 & covariateSummary$CovariateMeanWithOutcome <= 1
+  # create two plots -1 or less or g1
+  binary <- plotly::plot_ly(x = covariateSummary$CovariateMeanWithNoOutcome[ind],
+                            #size = covariateSummary$size[ind],
+                            showlegend = F) %>%
+    plotly::add_markers(y = covariateSummary$CovariateMeanWithOutcome[ind],
+                        color=factor(covariateSummary$color[ind]),
+                        text = paste(covariateSummary$annotation[ind]),
+                        showlegend = T
+    ) %>%
+    plotly::add_trace(x= c(0,1), y = c(0,1),mode = 'lines',
+                      line = list(dash = "dash"), color = I('black'),
+                      type='scatter', showlegend = FALSE) %>%
+    plotly::layout(#title = 'Prevalance of baseline predictors in persons with and without outcome',
+      xaxis = list(title = "Prevalance in persons without outcome",
+                   range = c(0, 1)),
+      yaxis = list(title = "Prevalance in persons with outcome",
+                   range = c(0, 1)),
+      legend = l, showlegend = T)
+  
+  if(sum(!ind)>0){
+    maxValue <- max(c(covariateSummary$CovariateMeanWithNoOutcome[!ind],
+                      covariateSummary$CovariateMeanWithOutcome[!ind]), na.rm = T)
+    meas <- plotly::plot_ly(x = covariateSummary$CovariateMeanWithNoOutcome[!ind] ) %>%
+      plotly::add_markers(y = covariateSummary$CovariateMeanWithOutcome[!ind],
+                          text = paste(covariateSummary$annotation[!ind])) %>%
+      plotly::add_trace(x= c(0,maxValue), y = c(0,maxValue),mode = 'lines',
+                        line = list(dash = "dash"), color = I('black'),
+                        type='scatter', showlegend = FALSE) %>%
+      plotly::layout(#title = 'Prevalance of baseline predictors in persons with and without outcome',
+        xaxis = list(title = "Mean in persons without outcome"),
+        yaxis = list(title = "Mean in persons with outcome"),
+        showlegend = FALSE)
+  } else {
+    meas <- NULL
+  }
+  
+  return(list(binary=binary,
+              meas = meas))
+}
+
+
+
+
+
+
+plotPredictedPDF <- function(evaluation, type='test', fileName=NULL){
+  ind <- evaluation$thresholdSummary$Eval==type
+  
+  x<- evaluation$thresholdSummary[ind,c('predictionThreshold','truePositiveCount','trueNegativeCount',
+                                        'falsePositiveCount','falseNegativeCount')]
+  x<- x[order(x$predictionThreshold,-x$truePositiveCount, -x$falsePositiveCount),]
+  x$out <- c(x$truePositiveCount[-length(x$truePositiveCount)]-x$truePositiveCount[-1], x$truePositiveCount[length(x$truePositiveCount)])
+  x$nout <- c(x$falsePositiveCount[-length(x$falsePositiveCount)]-x$falsePositiveCount[-1], x$falsePositiveCount[length(x$falsePositiveCount)])
+  
+  vals <- c()
+  for(i in 1:length(x$predictionThreshold)){
+    if(i!=length(x$predictionThreshold)){
+      upper <- x$predictionThreshold[i+1]} else {upper <- min(x$predictionThreshold[i]+0.01,1)}
+    val <- x$predictionThreshold[i]+runif(x$out[i])*(upper-x$predictionThreshold[i])
+    vals <- c(val, vals)
+  }
+  vals[!is.na(vals)]
+  
+  vals2 <- c()
+  for(i in 1:length(x$predictionThreshold)){
+    if(i!=length(x$predictionThreshold)){
+      upper <- x$predictionThreshold[i+1]} else {upper <- min(x$predictionThreshold[i]+0.01,1)}
+    val2 <- x$predictionThreshold[i]+runif(x$nout[i])*(upper-x$predictionThreshold[i])
+    vals2 <- c(val2, vals2)
+  }
+  vals2[!is.na(vals2)]
+  
+  x <- rbind(data.frame(variable=rep('outcome',length(vals)), value=vals),
+             data.frame(variable=rep('No outcome',length(vals2)), value=vals2)
+  )
+  
+  plot <- ggplot2::ggplot(x, ggplot2::aes(x=x$value,
+                                          group=x$variable,
+                                          fill=x$variable)) +
+    ggplot2::geom_density(ggplot2::aes(x=x$value, fill=x$variable), alpha=.3) +
+    ggplot2::scale_x_continuous("Prediction Threshold")+#, limits=c(0,1)) +
+    ggplot2::scale_y_continuous("Density") + 
+    ggplot2::guides(fill=ggplot2::guide_legend(title="Class"))
+  
+  if (!is.null(fileName))
+    ggplot2::ggsave(fileName, plot, width = 5, height = 4.5, dpi = 400)
+  return(plot)
+}
+
+
+plotPreferencePDF <- function(evaluation, type='test', fileName=NULL){
+  ind <- evaluation$thresholdSummary$Eval==type
+  
+  x<- evaluation$thresholdSummary[ind,c('preferenceThreshold','truePositiveCount','trueNegativeCount',
+                                        'falsePositiveCount','falseNegativeCount')]
+  x<- x[order(x$preferenceThreshold,-x$truePositiveCount),]
+  x$out <- c(x$truePositiveCount[-length(x$truePositiveCount)]-x$truePositiveCount[-1], x$truePositiveCount[length(x$truePositiveCount)])
+  x$nout <- c(x$falsePositiveCount[-length(x$falsePositiveCount)]-x$falsePositiveCount[-1], x$falsePositiveCount[length(x$falsePositiveCount)])
+  
+  vals <- c()
+  for(i in 1:length(x$preferenceThreshold)){
+    if(i!=length(x$preferenceThreshold)){
+      upper <- x$preferenceThreshold[i+1]} else {upper <- 1}
+    val <- x$preferenceThreshold[i]+runif(x$out[i])*(upper-x$preferenceThreshold[i])
+    vals <- c(val, vals)
+  }
+  vals[!is.na(vals)]
+  
+  vals2 <- c()
+  for(i in 1:length(x$preferenceThreshold)){
+    if(i!=length(x$preferenceThreshold)){
+      upper <- x$preferenceThreshold[i+1]} else {upper <- 1}
+    val2 <- x$preferenceThreshold[i]+runif(x$nout[i])*(upper-x$preferenceThreshold[i])
+    vals2 <- c(val2, vals2)
+  }
+  vals2[!is.na(vals2)]
+  
+  x <- rbind(data.frame(variable=rep('outcome',length(vals)), value=vals),
+             data.frame(variable=rep('No outcome',length(vals2)), value=vals2)
+  )
+  
+  plot <- ggplot2::ggplot(x, ggplot2::aes(x=x$value,
+                                          group=x$variable,
+                                          fill=x$variable)) +
+    ggplot2::geom_density(ggplot2::aes(x=x$value, fill=x$variable), alpha=.3) +
+    ggplot2::scale_x_continuous("Preference Threshold")+#, limits=c(0,1)) +
+    ggplot2::scale_y_continuous("Density") + 
+    ggplot2::guides(fill=ggplot2::guide_legend(title="Class"))
+  
+  if (!is.null(fileName))
+    ggplot2::ggsave(fileName, plot, width = 5, height = 4.5, dpi = 400)
+  return(plot)
+}
+
+
+
+
+plotDemographicSummary <- function(evaluation, type='test', fileName=NULL){
+  if (!all(is.na(evaluation$demographicSummary$averagePredictedProbability))){
+    ind <- evaluation$demographicSummary$Eval==type
+    x<- evaluation$demographicSummary[ind,colnames(evaluation$demographicSummary)%in%c('ageGroup','genGroup','averagePredictedProbability',
+                                                                                       'PersonCountAtRisk', 'PersonCountWithOutcome')]
+    
+    # remove -1 values:
+    x <- x[x$PersonCountWithOutcome != -1,]
+    if(nrow(x)==0){
+      return(NULL)
+    }
+    # end remove -1 values
+    
+    x$observed <- x$PersonCountWithOutcome/x$PersonCountAtRisk
+    
+    
+    x <- x[,colnames(x)%in%c('ageGroup','genGroup','averagePredictedProbability','observed')]
+    
+    # if age or gender missing add 
+    if(sum(colnames(x)=='ageGroup')==1 && sum(colnames(x)=='genGroup')==0  ){
+      x$genGroup = rep('Non', nrow(x))
+      evaluation$demographicSummary$genGroup = rep('Non', nrow(evaluation$demographicSummary))
+    } 
+    if(sum(colnames(x)=='ageGroup')==0 && sum(colnames(x)=='genGroup')==1  ){
+      x$ageGroup = rep('-1', nrow(x))
+      evaluation$demographicSummary$ageGroup = rep('-1', nrow(evaluation$demographicSummary))
+      
+    }
+    
+    x <- reshape2::melt(x, id.vars=c('ageGroup','genGroup'))
+    
+    # 1.96*StDevPredictedProbability
+    ci <- evaluation$demographicSummary[,colnames(evaluation$demographicSummary)%in%c('ageGroup','genGroup','averagePredictedProbability','StDevPredictedProbability')]
+    ci$StDevPredictedProbability[is.na(ci$StDevPredictedProbability)] <- 1
+    ci$lower <- ci$averagePredictedProbability-1.96*ci$StDevPredictedProbability
+    ci$lower[ci$lower <0] <- 0
+    ci$upper <- ci$averagePredictedProbability+1.96*ci$StDevPredictedProbability
+    ci$upper[ci$upper >1] <- max(ci$upper[ci$upper <1])
+    
+    x$age <- gsub('Age group:','', x$ageGroup)
+    x$age <- factor(x$age,levels=c(" 0-4"," 5-9"," 10-14",
+                                   " 15-19"," 20-24"," 25-29"," 30-34"," 35-39"," 40-44",
+                                   " 45-49"," 50-54"," 55-59"," 60-64"," 65-69"," 70-74",
+                                   " 75-79"," 80-84"," 85-89"," 90-94"," 95-99","-1"),ordered=TRUE)
+    
+    x <- merge(x, ci[,c('ageGroup','genGroup','lower','upper')], by=c('ageGroup','genGroup'))
+    
+    plot <- ggplot2::ggplot(data=x, 
+                            ggplot2::aes(x=age, group=variable*genGroup)) +
+      
+      ggplot2::geom_line(ggplot2::aes(y=value, group=variable,
+                                      color=variable,
+                                      linetype = variable))+
+      ggplot2::geom_ribbon(data=x[x$variable!='observed',],
+                           ggplot2::aes(ymin=lower, ymax=upper
+                                        , group=genGroup), 
+                           fill="blue", alpha="0.2") +
+      ggplot2::facet_grid(.~ genGroup, scales = "free") +
+      ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, hjust = 1)) +
+      #ggplot2::coord_flip() +
+      ggplot2::scale_y_continuous("Fraction") +
+      ggplot2::scale_x_discrete("Age") +
+      ggplot2::scale_color_manual(values = c("royalblue4","red"),
+                                  guide = ggplot2::guide_legend(title = NULL),
+                                  labels = c("Expected", "Observed")) +
+      
+      ggplot2::guides(linetype=FALSE)
+    
+    if (!is.null(fileName))
+      ggplot2::ggsave(fileName, plot, width = 7, height = 4.5, dpi = 400)
+    return(plot)
+  }
+}
+
+
+#=============
+# CALIBRATIONSUMMARY PLOTS 
+#=============
+#' Plot the calibration
+#'
+#' @details
+#' Create a plot showing the calibration
+#' #'
+#' @param evaluation            A prediction object as generated using the
+#'                              \code{\link{runPlp}} function.
+#' @param type                  options: 'train' or test'
+#' @param fileName              Name of the file where the plot should be saved, for example
+#'                              'plot.png'. See the function \code{ggsave} in the ggplot2 package for
+#'                              supported file formats.
+#'
+#' @return
+#' A ggplot object. Use the \code{\link[ggplot2]{ggsave}} function to save to file in a different
+#' format.
+#'
+#' @export
+plotSparseCalibration <- function(evaluation, type='test', fileName=NULL){
+  ind <- evaluation$calibrationSummary$Eval==type
+  
+  x<- evaluation$calibrationSummary[ind,c('averagePredictedProbability','observedIncidence')]
+  maxVal <- max(x$averagePredictedProbability,x$observedIncidence)
+  model <- stats::lm(observedIncidence~averagePredictedProbability, data=x)
+  res <- model$coefficients
+  names(res) <- c('Intercept','Gradient')
+  
+  # confidence int
+  interceptConf <- stats::confint(model)[1,]
+  gradientConf <- stats::confint(model)[2,]
+  
+  cis <- data.frame(lci = interceptConf[1]+seq(0,1,length.out = nrow(x))*gradientConf[1],
+                    uci = interceptConf[2]+seq(0,1,length.out = nrow(x))*gradientConf[2],
+                    x=seq(0,1,length.out = nrow(x)))
+  
+  x <- cbind(x, cis)
+  # TODO: CHECK INPUT
+  plot <- ggplot2::ggplot(data=x,
+                          ggplot2::aes(x=averagePredictedProbability, y=observedIncidence
+                          )) +
+    ggplot2::geom_ribbon(ggplot2::aes(ymin=lci,ymax=uci, x=x), 
+                         fill="blue", alpha="0.2") +
+    ggplot2::geom_point(size=1, color='darkblue') +
+    ggplot2::coord_cartesian(ylim = c(0, maxVal), xlim =c(0,maxVal)) +
+    ggplot2::geom_abline(intercept = 0, slope = 1, linetype = 2, size=1,
+                         show.legend = TRUE) +
+    ggplot2::geom_abline(intercept = res['Intercept'], slope = res['Gradient'],
+                         linetype = 1,show.legend = TRUE,
+                         color='darkblue') +
+    ggplot2::scale_x_continuous("Average Predicted Probability") +
+    ggplot2::scale_y_continuous("Observed Fraction With Outcome")
+  
+  
+  
+  if (!is.null(fileName))
+    ggplot2::ggsave(fileName, plot, width = 5, height = 3.5, dpi = 400)
+  return(plot)
+}
+
+#=============
+# CALIBRATIONSUMMARY PLOTS 2
+#=============
+#' Plot the conventional calibration
+#'
+#' @details
+#' Create a plot showing the calibration
+#' #'
+#' @param evaluation            A prediction object as generated using the
+#'                              \code{\link{runPlp}} function.
+#' @param type                  options: 'train' or test'
+#' @param fileName              Name of the file where the plot should be saved, for example
+#'                              'plot.png'. See the function \code{ggsave} in the ggplot2 package for
+#'                              supported file formats.
+#'
+#' @return
+#' A ggplot object. Use the \code{\link[ggplot2]{ggsave}} function to save to file in a different
+#' format.
+#'
+#' @export
+plotSparseCalibration2 <- function(evaluation, type='test', fileName=NULL){
+  ind <- evaluation$calibrationSummary$Eval==type
+  
+  x<- evaluation$calibrationSummary[ind,c('averagePredictedProbability','observedIncidence', 'PersonCountAtRisk')]
+  
+  
+  cis <- apply(x, 1, function(x) binom.test(x[2]*x[3], x[3], alternative = c("two.sided"), conf.level = 0.95)$conf.int)
+  x$lci <- cis[1,]  
+  x$uci <- cis[2,]
+  
+  maxes <- max(max(x$averagePredictedProbability), max(x$observedIncidence))*1.1
+  
+  # TODO: CHECK INPUT
+  limits <- ggplot2::aes(ymax = x$uci, ymin= x$lci)
+  
+  plot <- ggplot2::ggplot(data=x,
+                          ggplot2::aes(x=averagePredictedProbability, y=observedIncidence
+                          )) +
+    ggplot2::geom_point(size=2, color='black') +
+    ggplot2::geom_errorbar(limits) +
+    #ggplot2::geom_smooth(method=lm, se=F, colour='darkgrey') +
+    ggplot2::geom_line(colour='darkgrey') +
+    ggplot2::geom_abline(intercept = 0, slope = 1, linetype = 5, size=0.4,
+                         show.legend = TRUE) +
+    ggplot2::scale_x_continuous("Average Predicted Probability") +
+    ggplot2::scale_y_continuous("Observed Fraction With Outcome") +
+    ggplot2::coord_cartesian(xlim = c(0, maxes), ylim=c(0,maxes)) 
+  
+  
+  if (!is.null(fileName))
+    ggplot2::ggsave(fileName, plot, width = 5, height = 3.5, dpi = 400)
+  return(plot)
+}
+
+plotPredictionDistribution <- function(evaluation, type='test', fileName=NULL){
+  ind <- evaluation$predictionDistribution$Eval==type
+  x<- evaluation$predictionDistribution[ind,]
+  
+  #(x=Class, y=predictedProbabllity sequence:  min->P05->P25->Median->P75->P95->max)
+  
+  plot <-   ggplot2::ggplot(x, ggplot2::aes(x=as.factor(class),
+                                            ymin=MinPredictedProbability,
+                                            lower=P25PredictedProbability,
+                                            middle=MedianPredictedProbability,
+                                            upper=P75PredictedProbability, 
+                                            ymax=MaxPredictedProbability, 
+                                            color=as.factor(class))) + 
+    ggplot2::coord_flip() +
+    ggplot2::geom_boxplot(stat="identity")  +
+    ggplot2::scale_x_discrete("Class") + 
+    ggplot2::scale_y_continuous("Predicted Probability") + 
+    ggplot2::theme(legend.position="none") +
+    ggplot2::geom_segment(ggplot2::aes(x = 0.9, y = x$P05PredictedProbability[x$class==0], 
+                                       xend = 1.1, yend = x$P05PredictedProbability[x$class==0]), color='red') +
+    ggplot2::geom_segment(ggplot2::aes(x = 0.9, y = x$P95PredictedProbability[x$class==0], 
+                                       xend = 1.1, yend = x$P95PredictedProbability[x$class==0]), color='red') +
+    ggplot2::geom_segment(ggplot2::aes(x = 1.9, y = x$P05PredictedProbability[x$class==1], 
+                                       xend = 2.1, yend = x$P05PredictedProbability[x$class==1])) +
+    ggplot2::geom_segment(ggplot2::aes(x = 1.9, y = x$P95PredictedProbability[x$class==1], 
+                                       xend = 2.1, yend = x$P95PredictedProbability[x$class==1]))
+  
+  
+  if (!is.null(fileName))
+    ggplot2::ggsave(fileName, plot, width = 5, height = 4.5, dpi = 400)
+  return(plot)
+}
\ No newline at end of file
diff --git a/ehdenRaPrediction/server.R b/ehdenRaPrediction/server.R
new file mode 100644
index 00000000..d637e178
--- /dev/null
+++ b/ehdenRaPrediction/server.R
@@ -0,0 +1,317 @@
+# @file server.R
+#
+# Copyright 2018 Observational Health Data Sciences and Informatics
+#
+# This file is part of PatientLevelPrediction
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+library(shiny)
+library(plotly)
+library(shinycssloaders)
+
+source("utils.R")
+source("plots.R")
+
+shiny::shinyServer(function(input, output, session) {
+  session$onSessionEnded(stopApp)
+  # reactive values - contains the location of the plpResult
+  ##reactVars <- shiny::reactiveValues(resultLocation=NULL,
+  ##                                   plpResult= NULL)
+  #=============
+  
+  summaryData <- shiny::reactive({
+    ind <- 1:nrow(allPerformance)
+    if(input$devDatabase!='All'){
+      ind <- intersect(ind,which(as.character(allPerformance$devDatabase)==input$devDatabase))
+    }
+    if(input$valDatabase!='All'){
+      ind <- intersect(ind,which(as.character(allPerformance$valDatabase)==input$valDatabase))
+    }
+    if(input$T!='All'){
+      ind <- intersect(ind,which(allPerformance$cohortName==input$T))
+    }
+    if(input$O!='All'){
+      ind <- intersect(ind,which(allPerformance$outcomeName==input$O))
+    }
+    if(input$modelSettingName!='All'){
+      ind <- intersect(ind,which(as.character(allPerformance$modelSettingName)==input$modelSettingName))
+    }
+    if(input$riskWindowStart!='All'){
+      ind <- intersect(ind,which(allPerformance$riskWindowStart==input$riskWindowStart))
+    }
+    if(input$riskWindowEnd!='All'){
+      ind <- intersect(ind,which(allPerformance$riskWindowEnd==input$riskWindowEnd))
+    }
+    
+    ind
+  })
+  
+  
+  
+  output$summaryTable <- DT::renderDataTable(DT::datatable(formatPerformance[summaryData(),!colnames(formatPerformance)%in%c('addExposureDaysToStart','addExposureDaysToEnd')],
+                                                           rownames= FALSE, selection = 'single'))
+  
+  
+  dataofint <- shiny::reactive({
+    if(is.null(input$summaryTable_rows_selected[1])){
+      ind <- 1
+    }else{
+      ind <- input$summaryTable_rows_selected[1]
+    }
+    
+    loc <- plpResultLocation[summaryData(),][ind,]$plpResultLocation
+    logLocation <- gsub('validationResult.rds','plpLog.txt',gsub('plpResult.rds','plpLog.txt', as.character(loc)))
+    if(file.exists(logLocation)){
+      txt <- readLines(logLocation)
+    } else{
+      txt <- 'log not available'
+    }
+    
+    covariates <- NULL
+    population <- NULL
+    modelset <- NULL
+    
+    if(file.exists(as.character(loc))){
+      eval <- readRDS(as.character(loc))
+      # rounding values to 2dp
+      for(coln in c('covariateValue','CovariateMeanWithOutcome','CovariateMeanWithNoOutcome')){
+        eval$covariateSummary[,coln] <- format(round(eval$covariateSummary[,coln], 4), nsmall = 4)
+        class(eval$covariateSummary[,coln]) <- "numeric"
+      }
+      
+    } else{
+      eval <- NULL
+    }
+    if(length(grep('/Validation',loc))>0){
+      type <- 'validation' }else{
+        type <- 'test'
+      }
+    
+    if(!is.null(eval)){
+      covariates <- eval$model$metaData$call$covariateSettings
+      population <- eval$model$populationSettings
+      covariates <- data.frame(covariateName = names(covariates), 
+                               SettingValue = unlist(lapply(covariates, 
+                                                            function(x) paste0(x, 
+                                                                               collapse='-')))
+      )
+      population$attrition <- NULL # remove the attrition as result and not setting
+      population <- data.frame(Setting = names(population), 
+                               Value = unlist(lapply(population, 
+                                                     function(x) paste0(x, 
+                                                                        collapse='-')))
+      )
+      modelset <- data.frame(Setting = c('Model',names(eval$model$modelSettings[[2]])),
+                             Value = c(eval$model$modelSettings[[1]], unlist(lapply(eval$model$modelSettings[[2]], 
+                                                                                    function(x) paste0(x, collapse=''))))
+      )
+      
+      row.names(covariates) <- NULL
+      row.names(population) <- NULL
+      row.names(modelset) <- NULL
+    }
+    
+    return(list(eval=eval, type=type, 
+                logtext = txt,
+                logLocation=logLocation,
+                covariates = covariates,
+                population = population,
+                modelset = modelset))
+  })
+  
+  plotters <- shiny::reactive({
+    
+    eval <- dataofint()$eval$performanceEvaluation
+    if(is.null(eval)){return(NULL)}
+    
+    calPlot <- NULL 
+    rocPlot <- NULL
+    prPlot <- NULL
+    f1Plot <- NULL
+    demoPlot <- NULL
+    boxPlot <- NULL
+    distPlot <- NULL
+    txt <- 'Empty'
+    predictionText <- c()
+    
+    if(!is.null(eval)){
+      intPlot <- plotShiny(eval, input$slider1)
+      rocPlot <- intPlot$roc
+      prPlot <- intPlot$pr
+      f1Plot <- intPlot$f1score
+      threshold <- intPlot$threshold
+      prefthreshold <- intPlot$prefthreshold
+      TP <- intPlot$TP
+      FP <- intPlot$FP
+      TN <- intPlot$TN
+      FN <- intPlot$FN
+      prefdistPlot <- plotPreferencePDF(eval, type=dataofint()$type )
+      prefdistPlot <- prefdistPlot + ggplot2::geom_vline(xintercept=prefthreshold)
+      preddistPlot <- plotPredictedPDF(eval, type=dataofint()$type )
+      preddistPlot <- preddistPlot + ggplot2::geom_vline(xintercept=threshold)
+      boxPlot <-  plotPredictionDistribution(eval, type=dataofint()$type )
+      
+      calPlot <- plotSparseCalibration2(eval, type=dataofint()$type )
+      demoPlot <- tryCatch(plotDemographicSummary(eval, type=dataofint()$type ),
+                           error= function(cond){return(NULL)})
+      
+      if(is.null(input$summaryTable_rows_selected[1])){
+        ind <- 1
+      }else{
+        ind <- input$summaryTable_rows_selected[1]
+      }
+      predictionText <- paste0('Within ', formatPerformance[summaryData(),'T'][ind],
+                               ' predict who will develop ', formatPerformance[summaryData(),'O'][ind],
+                               ' during ', formatPerformance[summaryData(),'TAR start'][ind], ' day/s',
+                               ' after ', ifelse(formatPerformance[summaryData(),'addExposureDaysToStart'][ind]==0, ' cohort start ', ' cohort end '),
+                               ' and ', formatPerformance[summaryData(),'TAR end'][ind], ' day/s',
+                               ' after ', ifelse(formatPerformance[summaryData(),'addExposureDaysToEnd'][ind]==0, ' cohort start ', ' cohort end '))
+      
+    }
+    
+    twobytwo <- as.data.frame(matrix(c(FP,TP,TN,FN), byrow=T, ncol=2))
+    colnames(twobytwo) <- c('Ground Truth Negative','Ground Truth Positive')
+    rownames(twobytwo) <- c('Predicted Positive','Predicted Negative')
+    
+    performance <- data.frame(Incidence = (TP+FN)/(TP+TN+FP+FN),
+                              Threshold = threshold,
+                              Sensitivity = TP/(TP+FN),
+                              Specificity = TN/(TN+FP),
+                              PPV = TP/(TP+FP),
+                              NPV = TN/(TN+FN))
+    
+    list(rocPlot= rocPlot, calPlot=calPlot, 
+         prPlot=prPlot, f1Plot=f1Plot, 
+         demoPlot=demoPlot, boxPlot=boxPlot,
+         prefdistPlot=prefdistPlot,
+         preddistPlot=preddistPlot, predictionText=predictionText,
+         threshold = format(threshold, digits=5), 
+         twobytwo=twobytwo,
+         performance = performance )
+  })
+  
+  output$performance <- shiny::renderTable(plotters()$performance, 
+                                           rownames = F, digits = 3)
+  output$twobytwo <- shiny::renderTable(plotters()$twobytwo, 
+                                        rownames = T, digits = 0)
+  
+  output$modelTable <- DT::renderDataTable(dataofint()$modelset)
+  output$covariateTable <- DT::renderDataTable(dataofint()$covariates)
+  output$populationTable <- DT::renderDataTable(dataofint()$population)
+  
+  output$info <- shiny::renderText(plotters()$predictionText)
+  output$log <- shiny::renderText( paste(dataofint()$logtext, collapse="\n") )
+  output$threshold <- shiny::renderText(plotters()$threshold)
+  
+  output$roc <- plotly::renderPlotly({
+    plotters()$rocPlot
+  })
+  output$cal <- shiny::renderPlot({
+    plotters()$calPlot
+  })
+  output$pr <- plotly::renderPlotly({
+    plotters()$prPlot
+  })
+  output$f1 <- plotly::renderPlotly({
+    plotters()$f1Plot
+  })
+  output$demo <- shiny::renderPlot({
+    plotters()$demoPlot
+  })
+  output$box <- shiny::renderPlot({
+    plotters()$boxPlot
+  })
+  output$preddist <- shiny::renderPlot({
+    plotters()$preddistPlot
+  })
+  output$prefdist <- shiny::renderPlot({
+    plotters()$prefdistPlot
+  })
+  
+  
+  covs <- shiny::reactive({
+    if(is.null(dataofint()$eval))
+      return(NULL)
+    plotCovariateSummary(dataofint()$eval$covariateSummary)
+  })
+  
+  output$covariateSummaryBinary <- plotly::renderPlotly({ covs()$binary })
+  output$covariateSummaryMeasure <- plotly::renderPlotly({ covs()$meas })
+  
+  
+  output$modelView <- DT::renderDataTable(dataofint()$eval$covariateSummary[,c('covariateName','covariateValue','CovariateMeanWithOutcome','CovariateMeanWithNoOutcome' )],
+                                          colnames = c('Covariate Name', 'Value', 'Outcome Mean', 'Non-outcome Mean'))
+  
+  
+  # dashboard
+  
+  output$performanceBoxIncidence <- renderInfoBox({
+    infoBox(
+      "Incidence", paste0(round(plotters()$performance$Incidence*100, digits=3),'%'), icon = icon("ambulance"),
+      color = "green"
+    )
+  })
+  
+  output$performanceBoxThreshold <- renderInfoBox({
+    infoBox(
+      "Threshold", format((plotters()$performance$Threshold), scientific = F, digits=3), icon = icon("edit"),
+      color = "yellow"
+    )
+  })
+  
+  output$performanceBoxPPV <- renderInfoBox({
+    infoBox(
+      "PPV", paste0(round(plotters()$performance$PPV*1000)/10, "%"), icon = icon("thumbs-up"),
+      color = "orange"
+    )
+  })
+  
+  output$performanceBoxSpecificity <- renderInfoBox({
+    infoBox(
+      "Specificity", paste0(round(plotters()$performance$Specificity*1000)/10, "%"), icon = icon("bullseye"),
+      color = "purple"
+    )
+  })
+  
+  output$performanceBoxSensitivity <- renderInfoBox({
+    infoBox(
+      "Sensitivity", paste0(round(plotters()$performance$Sensitivity*1000)/10, "%"), icon = icon("low-vision"),
+      color = "blue"
+    )
+  })
+  
+  output$performanceBoxNPV <- renderInfoBox({
+    infoBox(
+      "NPV", paste0(round(plotters()$performance$NPV*1000)/10, "%"), icon = icon("minus-square"),
+      color = "black"
+    )
+  })
+  
+  
+  
+  # Downloadable csv of model ----
+  output$downloadData <- downloadHandler(
+    filename = function(){'model.csv'},
+    content = function(file) {
+      write.csv(dataofint()$eval$covariateSummary[dataofint()$eval$covariateSummary$covariateValue!=0,c('covariateName','covariateValue','CovariateMeanWithOutcome','CovariateMeanWithNoOutcome' )]
+                , file, row.names = FALSE)
+    }
+  )
+  
+  
+  
+  
+  #=============  
+  
+})
\ No newline at end of file
diff --git a/ehdenRaPrediction/ui.R b/ehdenRaPrediction/ui.R
new file mode 100644
index 00000000..8ed94d16
--- /dev/null
+++ b/ehdenRaPrediction/ui.R
@@ -0,0 +1,238 @@
+# @file Ui.R
+#
+# Copyright 2018 Observational Health Data Sciences and Informatics
+#
+# This file is part of PatientLevelPrediction
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+library(shiny)
+library(plotly)
+library(shinycssloaders)
+library(shinydashboard)
+
+ui <- shinydashboard::dashboardPage(skin = 'black',
+                                    
+                                    shinydashboard::dashboardHeader(title = "Multiple PLP Viewer", 
+                                                                    
+                                                                    tags$li(div(img(src = 'logo.png',
+                                                                                    title = "OHDSI PLP", height = "40px", width = "40px"),
+                                                                                style = "padding-top:0px; padding-bottom:0px;"),
+                                                                            class = "dropdown")
+                                                                    
+                                                                    
+                                    ), 
+                                    
+                                    shinydashboard::dashboardSidebar(
+                                      shinydashboard::sidebarMenu(
+                                        shinydashboard::menuItem("Summary", tabName = "Summary", icon = shiny::icon("table")),
+                                        shinydashboard::menuItem("Performance", tabName = "Performance", icon = shiny::icon("bar-chart")),
+                                        shinydashboard::menuItem("Model", tabName = "Model", icon = shiny::icon("clipboard")),
+                                        shinydashboard::menuItem("Log", tabName = "Log", icon = shiny::icon("list")),
+                                        shinydashboard::menuItem("Help", tabName = "Help", icon = shiny::icon("info"))
+                                      )
+                                    ),
+                                    
+                                    shinydashboard::dashboardBody(
+                                      shinydashboard::tabItems(
+                                        
+                                        # help tab
+                                        shinydashboard::tabItem(tabName = "Help",
+                                                                shiny::h2("Information"),
+                                                                shiny::p("Click on a row to explore the results for that model.  When you wish to explore a different model, then select the new result row and the tabs will be updated."),
+                                                                shiny::a("Demo Video", href = 'https://youtu.be/StpV40yl1UE', target='_blank')
+                                        ),
+                                        
+                                        # First tab content
+                                        shinydashboard::tabItem(tabName = "Summary",
+                                                                
+                                                                shiny::fluidRow(
+                                                                  shiny::column(2, 
+                                                                                shiny::h4('Filters'),
+                                                                                shiny::selectInput('devDatabase', 'Development Database', c('All',unique(as.character(allPerformance$devDatabase)))),
+                                                                                shiny::selectInput('valDatabase', 'Validation Database', c('All',unique(as.character(allPerformance$valDatabase)))),
+                                                                                shiny::selectInput('T', 'Target Cohort', c('All',unique(as.character(allPerformance$cohortName)))),
+                                                                                shiny::selectInput('O', 'Outcome Cohort', c('All',unique(as.character(allPerformance$outcomeName)))),
+                                                                                shiny::selectInput('riskWindowStart', 'Time-at-risk start:', c('All',unique(allPerformance$riskWindowStart))),
+                                                                                shiny::selectInput('riskWindowEnd', 'Time-at-risk end:', c('All',unique(as.character(allPerformance$riskWindowEnd)))),
+                                                                                shiny::selectInput('modelSettingName', 'Model:', c('All',unique(as.character(allPerformance$modelSettingName))))
+                                                                  ),  
+                                                                  shiny::column(10, style = "background-color:#F3FAFC;",
+                                                                                
+                                                                                # do this inside tabs:
+                                                                                shiny::tabsetPanel(
+                                                                                  
+                                                                                  shiny::tabPanel("Results",
+                                                                                                  shiny::div(DT::dataTableOutput('summaryTable'), 
+                                                                                                             style = "font-size:70%")),
+                                                                                  
+                                                                                  shiny::tabPanel("Model Settings",
+                                                                                                  shiny::h3('Model Settings: ', 
+                                                                                                            shiny::a("help", href="https://ohdsi.github.io/PatientLevelPrediction/reference/index.html", target="_blank") 
+                                                                                                  ),
+                                                                                                  DT::dataTableOutput('modelTable')),
+                                                                                  
+                                                                                  shiny::tabPanel("Population Settings",
+                                                                                                  shiny::h3('Population Settings: ', 
+                                                                                                            shiny::a("help", href="https://ohdsi.github.io/PatientLevelPrediction/reference/createStudyPopulation.html", target="_blank") 
+                                                                                                  ),
+                                                                                                  DT::dataTableOutput('populationTable')),
+                                                                                  
+                                                                                  shiny::tabPanel("Covariate Settings",
+                                                                                                  shiny::h3('Covariate Settings: ', 
+                                                                                                            shiny::a("help", href="http://ohdsi.github.io/FeatureExtraction/reference/createCovariateSettings.html", target="_blank") 
+                                                                                                  ),
+                                                                                                  DT::dataTableOutput('covariateTable'))
+                                                                                )
+                                                                                
+                                                                  )
+                                                                  
+                                                                )),
+                                        # second tab
+                                        shinydashboard::tabItem(tabName = "Performance", 
+                                                                
+                                                                shiny::fluidRow(
+                                                                  tabBox(
+                                                                    title = "Performance", 
+                                                                    # The id lets us use input$tabset1 on the server to find the current tab
+                                                                    id = "tabset1", height = "100%", width='100%',
+                                                                    tabPanel("Summary", 
+                                                                             
+                                                                             shiny::fluidRow(
+                                                                               shiny::column(width = 4,
+                                                                                             shinydashboard::box(width = 12,
+                                                                                                                 title = tagList(shiny::icon("question"),"Prediction Question"), status = "info", solidHeader = TRUE,
+                                                                                                                 shiny::textOutput('info')
+                                                                                             ),
+                                                                                             shinydashboard::box(width = 12,
+                                                                                                                 title = tagList(shiny::icon("gear"), "Input"), 
+                                                                                                                 status = "info", solidHeader = TRUE,
+                                                                                                                 shiny::splitLayout(
+                                                                                                                   cellWidths = c('5%', '90%', '5%'),
+                                                                                                                   shiny::h5(' '),
+                                                                                                                   shiny::sliderInput("slider1", 
+                                                                                                                                      shiny::h4("Threshold value slider: ", strong(shiny::textOutput('threshold'))), 
+                                                                                                                                      min = 1, max = 100, value = 50, ticks = F),
+                                                                                                                   shiny::h5(' ')
+                                                                                                                 ),
+                                                                                                                 shiny::splitLayout(
+                                                                                                                   cellWidths = c('5%', '90%', '5%'),
+                                                                                                                   shiny::h5(strong('0')),
+                                                                                                                   shiny::h5(' '),
+                                                                                                                   shiny::h5(strong('1'))
+                                                                                                                 ),
+                                                                                                                 shiny::tags$script(shiny::HTML("
+                                                                                                                                                $(document).ready(function() {setTimeout(function() {
+                                                                                                                                                supElement = document.getElementById('slider1').parentElement;
+                                                                                                                                                $(supElement).find('span.irs-max, span.irs-min, span.irs-single, span.irs-from, span.irs-to').remove();
+                                                                                                                                                }, 50);})
+                                                                                                                                                "))
+                                                                                                                 )
+                                                                                             
+                                                                                             ),
+                                                                               
+                                                                               
+                                                                               shiny::column(width = 8,
+                                                                                             shinydashboard::box(width = 12,
+                                                                                                                 title = "Dashboard",
+                                                                                                                 status = "warning", solidHeader = TRUE,
+                                                                                                                 shinydashboard::infoBoxOutput("performanceBoxThreshold"),
+                                                                                                                 shinydashboard::infoBoxOutput("performanceBoxIncidence"),
+                                                                                                                 shinydashboard::infoBoxOutput("performanceBoxPPV"),
+                                                                                                                 shinydashboard::infoBoxOutput("performanceBoxSpecificity"),
+                                                                                                                 shinydashboard::infoBoxOutput("performanceBoxSensitivity"),
+                                                                                                                 shinydashboard::infoBoxOutput("performanceBoxNPV")
+                                                                                                                 
+                                                                                             ),
+                                                                                             shinydashboard::box(width = 12,
+                                                                                                                 title = "Cutoff Performance",
+                                                                                                                 status = "warning", solidHeader = TRUE,
+                                                                                                                 shiny::tableOutput('twobytwo')
+                                                                                                                 #infoBoxOutput("performanceBox"),
+                                                                                             )
+                                                                               )
+                                                                               )
+                                                                             
+                                                                             
+                                                                               ),
+                                                                    tabPanel("Discrimination", 
+                                                                             
+                                                                             shiny::fluidRow(
+                                                                               shinydashboard::box( status = 'info',
+                                                                                                    title = "ROC Plot", solidHeader = TRUE,
+                                                                                                    shinycssloaders::withSpinner(plotly::plotlyOutput('roc'))),
+                                                                               shinydashboard::box(status = 'info',
+                                                                                                   title = "Precision recall plot", solidHeader = TRUE,
+                                                                                                   side = "right",
+                                                                                                   shinycssloaders::withSpinner(plotly::plotlyOutput('pr')))),
+                                                                             
+                                                                             shiny::fluidRow(
+                                                                               shinydashboard::box(status = 'info',
+                                                                                                   title = "F1 Score Plot", solidHeader = TRUE,
+                                                                                                   shinycssloaders::withSpinner(plotly::plotlyOutput('f1'))),
+                                                                               shinydashboard::box(status = 'info',
+                                                                                                   title = "Box Plot", solidHeader = TRUE,
+                                                                                                   side = "right",
+                                                                                                   shinycssloaders::withSpinner(shiny::plotOutput('box')))),
+                                                                             
+                                                                             shiny::fluidRow(
+                                                                               shinydashboard::box(status = 'info',
+                                                                                                   title = "Prediction Score Distribution", solidHeader = TRUE,
+                                                                                                   shinycssloaders::withSpinner(shiny::plotOutput('preddist'))),
+                                                                               shinydashboard::box(status = 'info',
+                                                                                                   title = "Preference Score Distribution", solidHeader = TRUE,
+                                                                                                   side = "right",
+                                                                                                   shinycssloaders::withSpinner(shiny::plotOutput('prefdist'))))
+                                                                             
+                                                                             
+                                                                    ),
+                                                                    tabPanel("Calibration", 
+                                                                             shiny::fluidRow(
+                                                                               shinydashboard::box(status = 'info',
+                                                                                                   title = "Calibration Plot", solidHeader = TRUE,
+                                                                                                   shinycssloaders::withSpinner(shiny::plotOutput('cal'))),
+                                                                               shinydashboard::box(status = 'info',
+                                                                                                   title = "Demographic Plot", solidHeader = TRUE,
+                                                                                                   side = "right",
+                                                                                                   shinycssloaders::withSpinner(shiny::plotOutput('demo')))
+                                                                             )
+                                                                    )
+                                                                                             ))),
+                                        
+                                        # 3rd tab
+                                        shinydashboard::tabItem(tabName = "Model", 
+                                                                shiny::fluidRow(
+                                                                  shinydashboard::box( status = 'info',
+                                                                                       title = "Binary", solidHeader = TRUE,
+                                                                                       shinycssloaders::withSpinner(plotly::plotlyOutput('covariateSummaryBinary'))),
+                                                                  shinydashboard::box(status = 'info',
+                                                                                      title = "Measurements", solidHeader = TRUE,
+                                                                                      side = "right",
+                                                                                      shinycssloaders::withSpinner(plotly::plotlyOutput('covariateSummaryMeasure')))),
+                                                                
+                                                                shiny::fluidRow(width=12,
+                                                                                shinydashboard::box(status = 'info', width = 12,
+                                                                                                    title = "Model Table", solidHeader = TRUE,
+                                                                                                    shiny::downloadButton("downloadData", "Download Model"),
+                                                                                                    DT::dataTableOutput('modelView')))
+                                        ),
+                                        
+                                        # 4th tab
+                                        shinydashboard::tabItem(tabName = "Log", 
+                                                                shiny::verbatimTextOutput('log')
+                                        )
+                                        
+                                        
+                                                                )
+                                    )
+                                    )
\ No newline at end of file
diff --git a/ehdenRaPrediction/utils.R b/ehdenRaPrediction/utils.R
new file mode 100644
index 00000000..8de84575
--- /dev/null
+++ b/ehdenRaPrediction/utils.R
@@ -0,0 +1,121 @@
+summaryPlpAnalyses <- function(analysesLocation){ 
+  # loads the analyses and validations to get summaries
+  #========================================
+  settings <- read.csv(file.path(analysesLocation,'settings.csv'))
+  settings <- settings[,!colnames(settings)%in%c('plpDataFolder','studyPopFile','plpResultFolder')]
+  settings$analysisId <- paste0('Analysis_',  settings$analysisId)
+  
+  analysisIds <- dir(file.path(analysesLocation), recursive = F, full.names = T)
+  analysisIds <- analysisIds[grep('Analysis_',analysisIds)]
+  if(is.null(settings$devDatabase)){
+    settings$devDatabase <- 'Missing'
+  }
+  settings$valDatabase <- settings$devDatabase
+  devPerformance <- do.call(rbind,lapply(file.path(analysisIds), getPerformance))
+  devPerformance <- merge(settings[,c('analysisId','modelSettingsId', 'cohortName', 'outcomeName',
+                                      'populationSettingId','modelSettingName','addExposureDaysToStart',
+                                      'riskWindowStart', 'addExposureDaysToEnd',
+                                      'riskWindowEnd','devDatabase','valDatabase')],
+                          devPerformance, by='analysisId', all.x=T)
+  
+  validationLocation <- file.path(analysesLocation,'Validation')
+  if(length(dir(validationLocation))>0){
+    valPerformances <- c()
+    valDatabases <- dir(validationLocation, recursive = F, full.names = T)
+    for( valDatabase in valDatabases){
+      
+      valAnalyses <-  dir(valDatabase, recursive = F, full.names = T)
+      valAnalyses <-  valAnalyses[grep('Analysis_', valAnalyses)]
+      valPerformance <- do.call(rbind,lapply(file.path(valAnalyses), function(x) getValidationPerformance(x)))
+      valSettings <- settings[,c('analysisId','modelSettingsId', 'cohortName', 'outcomeName',
+                                 'populationSettingId','modelSettingName','addExposureDaysToStart',
+                                 'riskWindowStart', 'addExposureDaysToEnd',
+                                 'riskWindowEnd')]
+      valSettings$devDatabase <- settings$devDatabase[1]  
+      valPerformance <- merge(valSettings,
+                              valPerformance, by='analysisId')
+      valPerformance <- valPerformance[,colnames(devPerformance)] # make sure same order
+      valPerformances <- rbind(valPerformances, valPerformance)
+    }
+    
+    if(ncol(valPerformances)==ncol(devPerformance)){
+      allPerformance <- rbind(devPerformance,valPerformances)
+    } else{
+      stop('Issue with dev and val performance data.frames')
+    }
+  } else {
+    allPerformance <- devPerformance
+  }
+  
+  allPerformance$AUC <- as.double(allPerformance$AUC)
+  allPerformance$AUPRC <- as.double(allPerformance$AUPRC)
+  allPerformance$outcomeCount <- as.double(allPerformance$outcomeCount)
+  allPerformance$populationSize <- as.double(allPerformance$populationSize)
+  allPerformance$incidence <- as.double(allPerformance$incidence)
+  return(allPerformance)
+}
+
+getPerformance <- function(analysisLocation){
+  location <- file.path(analysisLocation, 'plpResult.rds')
+  if(!file.exists(location)){
+    analysisId <- strsplit(analysisLocation, '/')[[1]]
+    return(data.frame(analysisId=analysisId[length(analysisId)], 
+                      AUC=0.000, AUPRC=0, outcomeCount=0,
+                      populationSize=0,incidence=0,plpResultLocation=location, 
+                      plpResultLoad='loadPlpResult'))
+  }
+  # read rds here
+  res <- readRDS(file.path(analysisLocation,'plpResult.rds'))
+  res <- as.data.frame(res$performanceEvaluation$evaluationStatistics)
+  
+  #if empty do edit?
+  
+  res <- tryCatch(reshape2::dcast(res[res$Eval=='test',], analysisId ~ Metric, value.var='Value'),
+                  error = function(cont) return(NULL))
+  if(is.null(res)){
+    return(NULL) }
+  res <- res[,!colnames(res)%in%c("BrierScore","BrierScaled")]
+  res$incidence <- as.double(res$outcomeCount)/as.double(res$populationSize)*100
+  res[, !colnames(res)%in%c('analysisId','outcomeCount','populationSize')] <- 
+    format(as.double(res[, !colnames(res)%in%c('analysisId','outcomeCount','populationSize')]), digits = 2, scientific = F) 
+  
+  if(sum(colnames(res)=='AUC.auc_ub95ci')>0){
+    res$AUC <- res$AUC.auc
+    #res$AUC <- paste0(res$AUC.auc, ' (', res$AUC.auc_lb95ci,'-', res$AUC.auc_ub95ci,')')
+  }
+  
+  res$plpResultLocation <- location
+  res$plpResultLoad <- 'readRDS'#'loadPlpResult'
+  return(res[,c('analysisId', 'AUC', 'AUPRC', 'outcomeCount','populationSize','incidence','plpResultLocation', 'plpResultLoad')])
+}
+
+getValidationPerformance <- function(validationLocation){
+  val <- readRDS(file.path(validationLocation,'validationResult.rds'))
+  if("performanceEvaluation"%in%names(val)){
+    valPerformance <- reshape2::dcast(as.data.frame(val$performanceEvaluation$evaluationStatistics), 
+                                      analysisId ~ Metric, value.var='Value')
+  } else {
+    valPerformance <- reshape2::dcast(as.data.frame(val[[1]]$performanceEvaluation$evaluationStatistics), 
+                                      analysisId ~ Metric, value.var='Value')  
+  }
+  valPerformance$incidence <- as.double(valPerformance$outcomeCount)/as.double(valPerformance$populationSize)*100
+  valPerformance[, !colnames(valPerformance)%in%c('analysisId','outcomeCount','populationSize')] <- 
+    format(as.double(valPerformance[, !colnames(valPerformance)%in%c('analysisId','outcomeCount','populationSize')]), digits = 2, scientific = F) 
+  
+  if(sum(colnames(valPerformance)=='AUC.auc_ub95ci')>0){
+    valPerformance$AUC <- valPerformance$AUC.auc
+    #valPerformance$AUC <- paste0(valPerformance$AUC.auc, ' (', valPerformance$AUC.auc_lb95ci,'-', valPerformance$AUC.auc_ub95ci,')')
+  }
+  valPerformance$analysisId <- strsplit(validationLocation, '/')[[1]][[length(strsplit(validationLocation, '/')[[1]])]]
+  valPerformance$valDatabase <- strsplit(validationLocation, '/')[[1]][[length(strsplit(validationLocation, '/')[[1]])-1]]
+  valPerformance <- valPerformance[,c('analysisId','valDatabase', 'AUC', 'AUPRC', 'outcomeCount','populationSize','incidence')]
+  valPerformance$plpResultLocation <- file.path(validationLocation,'validationResult.rds')
+  valPerformance$plpResultLoad <- 'readRDS'
+  #valPerformance$rocplot <- file.path(validationLocation,'plots','sparseROC.pdf')
+  #valPerformance$calplot <- file.path(validationLocation,'plots','sparseCalibrationConventional.pdf')
+  return(valPerformance)
+}
+
+
+
+
diff --git a/ehdenRaPrediction/www/about.png b/ehdenRaPrediction/www/about.png
new file mode 100644
index 00000000..c56d5b02
Binary files /dev/null and b/ehdenRaPrediction/www/about.png differ
diff --git a/ehdenRaPrediction/www/custom.css b/ehdenRaPrediction/www/custom.css
new file mode 100644
index 00000000..26730bbd
--- /dev/null
+++ b/ehdenRaPrediction/www/custom.css
@@ -0,0 +1,81 @@
+.plotly.html-widget.html-widget-output.shiny-bound-output.js-plotly-plot {
+  z-index: 22;
+  position: relative; 
+}
+
+.plotlybars {
+  padding: 0 10px;
+  vertical-align: bottom;
+  width: 100%;
+  height: 100%;
+  overflow: hidden;
+  position: relative;
+  box-sizing: border-box;
+}
+
+.plotlybars-wrapper {
+  width: 165px;
+  height: 100px;
+  margin: 0 auto;
+  left: 0;
+  right: 0;
+  position: absolute;
+  z-index: 1;
+}
+
+.plotlybars-text {
+  color: #447adb;
+  font-family: 'Open Sans', verdana, arial, sans-serif;
+  font-size: 80%;
+  text-align: center;
+  margin-top: 5px;
+}
+
+.plotlybars-bar {
+  background-color: #447adb;
+  height: 100%;
+  width: 13.3%;
+  position: absolute;
+
+  -webkit-transform: translateZ(0);
+  transform: translateZ(0);
+
+  animation-duration: 2s;
+  animation-iteration-count: infinite;
+  animation-direction: normal;
+  animation-timing-function: linear;
+
+  -webkit-animation-duration: 2s;
+  -webkit-animation-iteration-count: infinite;
+  -webkit-animation-direction: normal;
+  -webkit-animation-timing-function: linear;
+}
+
+.b1 { left: 0%; top: 88%; animation-name: b1; -webkit-animation-name: b1; }
+.b2 { left: 14.3%; top: 76%; animation-name: b2; -webkit-animation-name: b2; }
+.b3 { left: 28.6%; top: 16%; animation-name: b3; -webkit-animation-name: b3; }
+.b4 { left: 42.9%; top: 40%; animation-name: b4; -webkit-animation-name: b4; }
+.b5 { left: 57.2%; top: 26%; animation-name: b5; -webkit-animation-name: b5; }
+.b6 { left: 71.5%; top: 67%; animation-name: b6; -webkit-animation-name: b6; }
+.b7 { left: 85.8%; top: 89%; animation-name: b7; -webkit-animation-name: b7; }
+
+@keyframes b1 { 0% { top: 88%; } 44% { top: 0%; } 94% { top: 100%; } 100% { top: 88%; } }
+@-webkit-keyframes b1 { 0% { top: 88%; } 44% { top: 0%; } 94% { top: 100%; } 100% { top: 88%; } }
+
+@keyframes b2 { 0% { top: 76%; } 38% { top: 0%; } 88% { top: 100%; } 100% { top: 76%; } }
+@-webkit-keyframes b2 { 0% { top: 76%; } 38% { top: 0%; } 88% { top: 100%; } 100% { top: 76%; } }
+
+@keyframes b3 { 0% { top: 16%; } 8% { top: 0%; } 58% { top: 100%; } 100% { top: 16%; } }
+@-webkit-keyframes b3 { 0% { top: 16%; } 8% { top: 0%; } 58% { top: 100%; } 100% { top: 16%; } }
+
+@keyframes b4 { 0% { top: 40%; } 20% { top: 0%; } 70% { top: 100%; } 100% { top: 40%; } }
+@-webkit-keyframes b4 { 0% { top: 40%; } 20% { top: 0%; } 70% { top: 100%; } 100% { top: 40%; } }
+
+@keyframes b5 { 0% { top: 26%; } 13% { top: 0%; } 63% { top: 100%; } 100% { top: 26%; } }
+@-webkit-keyframes b5 { 0% { top: 26%; } 13% { top: 0%; } 63% { top: 100%; } 100% { top: 26%; } }
+
+@keyframes b6 { 0% { top: 67%; } 33.5% { top: 0%; } 83% { top: 100%; } 100% { top: 67%; } }
+@-webkit-keyframes b6 { 0% { top: 67%; } 33.5% { top: 0%; } 83% { top: 100%; } 100% { top: 67%; } }
+
+@keyframes b7 { 0% { top: 89%; } 44.5% { top: 0%; } 94.5% { top: 100%; } 100% { top: 89%; } }
+@-webkit-keyframes b7 { 0% { top: 89%; } 44.5% { top: 0%; } 94.5% { top: 100%; } 100% { top: 89%; } }
diff --git a/ehdenRaPrediction/www/favicon.ico b/ehdenRaPrediction/www/favicon.ico
new file mode 100644
index 00000000..4421f8ef
Binary files /dev/null and b/ehdenRaPrediction/www/favicon.ico differ
diff --git a/ehdenRaPrediction/www/logo.png b/ehdenRaPrediction/www/logo.png
new file mode 100644
index 00000000..1392aa83
Binary files /dev/null and b/ehdenRaPrediction/www/logo.png differ