diff --git a/DHARMa/vignettes/DHARMa.Rmd b/DHARMa/vignettes/DHARMa.Rmd
index 411c48f..9e38190 100644
--- a/DHARMa/vignettes/DHARMa.Rmd
+++ b/DHARMa/vignettes/DHARMa.Rmd
@@ -11,8 +11,8 @@ vignette: >
   %\VignetteIndexEntry{Vignette for the DHARMa package}
   \usepackage[utf8]{inputenc}
   %\VignetteEngine{knitr::rmarkdown}
-abstract: "The 'DHARMa' package uses a simulation-based approach to create  readily  interpretable scaled (quantile) residuals for fitted generalized linear (mixed)  models. Currently supported are linear and generalized linear (mixed) models from  'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB', 'GLMMadaptive' and 'spaMM'; phylogenetic linear models from 'phylolm' (classes 'phylolm' and 'phyloglm'); generalized additive  models ('gam' from 'mgcv'); 'glm' (including 'negbin' from 'MASS', but excluding  quasi-distributions) and 'lm' model classes. Moreover, externally created simulations,  e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN',  or 'BUGS' can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial, temporal and phylogenetic autocorrelation. \n \n \n"
-editor_options:
+abstract: "The 'DHARMa' package uses a simulation-based approach to create  readily interpretable scaled (quantile) residuals for fitted generalized linear (mixed) models. Currently supported are linear and generalized linear (mixed) models from 'lme4' (classes 'lmerMod', 'glmerMod'), 'glmmTMB', 'GLMMadaptive' and 'spaMM', generalized additive models ('gam' from 'mgcv'), 'glm' (including 'negbin' from 'MASS', but excluding quasi-distributions) and 'lm' model classes. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as 'JAGS', 'STAN', or 'BUGS' can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial and temporal autocorrelation. \n \n \n"
+editor_options: 
   chunk_output_type: console
 ---
 
diff --git a/DHARMa/vignettes/DHARMaForBayesians.R b/DHARMa/vignettes/DHARMaForBayesians.R
deleted file mode 100644
index f9b0a4a..0000000
--- a/DHARMa/vignettes/DHARMaForBayesians.R
+++ /dev/null
@@ -1,78 +0,0 @@
-## ----global_options, include=FALSE--------------------------------------------
-knitr::opts_chunk$set(fig.width=8.5, fig.height=5.5, fig.align='center', warning=FALSE, message=FALSE)
-
-## ----echo = F, message = F----------------------------------------------------
-library(DHARMa)
-set.seed(123)
-
-## ----eval = F-----------------------------------------------------------------
-#  library(rjags)
-#  library(BayesianTools)
-#  
-#  set.seed(123)
-#  
-#  dat <- DHARMa::createData(200, overdispersion = 0.2)
-#  
-#  Data = as.list(dat)
-#  Data$nobs = nrow(dat)
-#  Data$nGroups = length(unique(dat$group))
-#  
-#  modelCode = "model{
-#  
-#    for(i in 1:nobs){
-#      observedResponse[i] ~ dpois(lambda[i])  # poisson error distribution
-#      lambda[i] <- exp(eta[i]) # inverse link function
-#      eta[i] <- intercept + env*Environment1[i]  # linear predictor
-#    }
-#  
-#    intercept ~ dnorm(0,0.0001)
-#    env ~ dnorm(0,0.0001)
-#  
-#    # Posterior predictive simulations
-#    for (i in 1:nobs) {
-#      observedResponseSim[i]~dpois(lambda[i])
-#    }
-#  
-#  }"
-#  
-#  jagsModel <- jags.model(file= textConnection(modelCode), data=Data, n.chains = 3)
-#  para.names <- c("intercept","env", "lambda", "observedResponseSim")
-#  Samples <- coda.samples(jagsModel, variable.names = para.names, n.iter = 5000)
-#  
-#  x = BayesianTools::getSample(Samples)
-#  
-#  colnames(x) # problem: all the variables are in one array - this is better in STAN, where this is a list - have to extract the right columns by hand
-#  posteriorPredDistr = x[,3:202] # this is the uncertainty of the mean prediction (lambda)
-#  posteriorPredSim = x[,203:402] # these are the simulations
-#  
-#  sim = createDHARMa(simulatedResponse = t(posteriorPredSim), observedResponse = dat$observedResponse, fittedPredictedResponse = apply(posteriorPredDistr, 2, median), integerResponse = T)
-#  plot(sim)
-
-## ----eval=F-------------------------------------------------------------------
-#    # Posterior predictive simulations
-#    for (i in 1:nobs) {
-#      observedResponseSim[i]~dpois(lambda[i])
-#    }
-
-## ----eval = F-----------------------------------------------------------------
-#    for(i in 1:nobs){
-#      observedResponse[i] ~ dpois(lambda[i])  # poisson error distribution
-#      lambda[i] <- exp(eta[i]) # inverse link function
-#      eta[i] <- intercept + env*Environment1[i] + RE[group[i]] # linear predictor
-#    }
-#  
-#    for(j in 1:nGroups){
-#     RE[j] ~ dnorm(0,tauRE)
-#    }
-
-## ----eval=F-------------------------------------------------------------------
-#    for(j in 1:nGroups){
-#     RESim[j] ~ dnorm(0,tauRE)
-#    }
-#  
-#    for (i in 1:nobs) {
-#      observedResponseSim[i] ~ dpois(lambdaSim[i])
-#      lambdaSim[i] <- exp(etaSim[i])
-#      etaSim[i] <- intercept + env*Environment1[i] + RESim[group[i]]
-#    }
-