update spelling mistakes

BayesianTools/R/VSEM.R

@@ -141,7 +141,7 @@ VSEMgetDefaults <- function(){
 
 #' Allows to mix a given parameter vector with a default parameter vector
 #' @param pars vector with new parameter values
-#' @param defaults vector with defaukt parameter values
+#' @param defaults vector with default parameter values
 #' @param locations indices of the new parameter values
 #' @rdname package-deprecated
 #' @description This function is deprecated and will be removed by v0.2. 
@@ -166,7 +166,7 @@ VSEMcreatePAR <- function(days = 1:(3*365)){
 
 #' Create an example dataset, and from that a likelihood or posterior for the VSEM model
 #' @author Florian Hartig
-#' @param likelihoodOnly switch to devide whether to create only a likelihood, or a full bayesianSetup with uniform priors.
+#' @param likelihoodOnly switch to decide whether to create only a likelihood, or a full bayesianSetup with uniform priors.
 #' @param plot switch to decide whether data should be plotted
 #' @param selection vector containing the indices of the selected parameters
 #' @details The purpose of this function is to be able to conveniently create a likelihood for the VSEM model for demonstration purposes. The function creates example data --> likelihood --> BayesianSetup, where the latter is the 

BayesianTools/inst/examples/marginalLikelihoodHelp.R

@@ -76,7 +76,7 @@ exp(M1$ln.ML - M2$ln.ML)
 
 # it has therefore been suggested that ML should not be calculated on uninformative priors. But
 # what to do if there are no informative priors?
-# one option is to calculate the fractional BF, which means that one splites the data in half,
+# one option is to calculate the fractional BF, which means that one splits the data in half,
 # uses the first half to fit the model, and then use the posterior as a new (now informative)
 # prior for the ML - let's do this for the previous case
 
@@ -145,7 +145,7 @@ exp(M1$ln.ML - M2$ln.ML)
 # Low dimensional case with narrow priors - all methods have low error
 
 # we use a truncated normal for the likelihood to make sure that the density 
-# integrates to 1 - makes it easier to calcuate the theoretical ML
+# integrates to 1 - makes it easier to calculate the theoretical ML
 likelihood <- function(x) sum(msm::dtnorm(x, log = TRUE, lower = -1, upper = 1))
 prior = createUniformPrior(lower = rep(-1,2), upper = rep(1,2))
 bayesianSetup <- createBayesianSetup(likelihood = likelihood, prior = prior)

BayesianTools/vignettes/BayesianTools.Rmd

@@ -82,7 +82,7 @@ Once you have your setup, you may want to run a calibration. The runMCMC functio
 - The sampler name
 - A list with settings - if a parameter is not provided, the default will be used
 
-As an example, choosing the sampler name "Metropolis" calls a versatile Metropolis-type MCMC with options for covariance adaptation, delayed rejection, tempering and Metropolis-within-Gibbs sampling. For details, see the the later reference on MCMC samplers. This is how we would call this sampler with default settings
+As an example, choosing the sampler name "Metropolis" calls a versatile Metropolis-type MCMC with options for covariance adaptation, delayed rejection, tempering and Metropolis-within-Gibbs sampling. For details, see the later reference on MCMC samplers. This is how we would call this sampler with default settings
 
 ```{r}
 iter = 10000
@@ -99,7 +99,7 @@ print(out)
 summary(out)
 ```
 
-and plottted with several plot functions. The marginalPlot can either be plotted as histograms with density overlay, which is also the default, or as a violin plot (see "?marginalPlot").
+and plotted with several plot functions. The marginalPlot can either be plotted as histograms with density overlay, which is also the default, or as a violin plot (see "?marginalPlot").
 
 ```{r}
 plot(out) # plot internally calls tracePlot(out)