update vignettes

.Rbuildignore

@@ -1,3 +1,5 @@
+^Meta$
+^doc$
 ^.*\.Rproj$
 ^.*\.RData$
 ^.*\.Rhistory$

.gitignore

@@ -1,3 +1,4 @@
+Meta
 .Rproj.user
 .Rhistory
 .RData

inst/doc/brms_customfamilies.Rmd → doc/brms_customfamilies.Rmd

@@ -5,16 +5,14 @@ date: "`r Sys.Date()`"
 output: 
   rmarkdown::html_vignette:
     toc: yes
+vignette: >
+  %\VignetteIndexEntry{Define Custom Response Distributions with brms}
+  %\VignetteEngine{knitr::rmarkdown}
+  \usepackage[utf8]{inputenc}
 params:
   EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true")
 ---
 
-<!--
-%\VignetteEngine{knitr::rmarkdown}
-%\VignetteIndexEntry{Define custom response distributions with brms}
-%\VignetteEncoding{UTF-8}
--->
-
 ```{r, SETTINGS-knitr, include=FALSE}
 stopifnot(require(knitr))
 options(width = 90)

inst/doc/brms_distreg.R → doc/brms_distreg.R

@@ -45,7 +45,7 @@ hyp <- "exp(sigma_Intercept + sigma_grouptreat) > exp(sigma_Intercept)"
 plot(hyp, chars = NULL)
 
 ## ---------------------------------------------------------------------------------------
-zinb <- read.csv("http://stats.idre.ucla.edu/stat/data/fish.csv")
+zinb <- read.csv("https://paul-buerkner.github.io/data/fish.csv")
 head(zinb)
 
 ## ---- results='hide'--------------------------------------------------------------------

inst/doc/brms_distreg.Rmd → doc/brms_distreg.Rmd

@@ -5,16 +5,14 @@ date: "`r Sys.Date()`"
 output: 
   rmarkdown::html_vignette:
     toc: yes
+vignette: >
+  %\VignetteIndexEntry{Estimating Distributional Models with brms}
+  %\VignetteEngine{knitr::rmarkdown}
+  \usepackage[utf8]{inputenc}
 params:
   EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true")
 ---
 
-<!--
-%\VignetteEngine{knitr::rmarkdown}
-%\VignetteIndexEntry{Fit Distributional Models with brms}
-%\VignetteEncoding{UTF-8}
--->
-
 ```{r, SETTINGS-knitr, include=FALSE}
 stopifnot(require(knitr))
 options(width = 90)
@@ -96,7 +94,7 @@ Indeed, the residual standard deviation of the treatment group seems to larger t
 Another important application of the distributional regression framework are so called zero-inflated models. These models are helpful whenever there are more zeros in the response variable than one would naturally expect. For example, if one seeks to predict the number of cigarettes people smoke per day and also includes non-smokers, there will be a huge amount of zeros which, when not modeled appropriately, can seriously distort parameter estimates. Here, we consider an example dealing with the number of fish caught by various groups of people. On the UCLA website (\url{https://stats.idre.ucla.edu/stata/dae/zero-inflated-poisson-regression}), the data are described as follows: "The state wildlife biologists want to model how many fish are being caught by fishermen at a state park. Visitors are asked how long they stayed, how many people were in the group, were there children in the group and how many fish were caught. Some visitors do not fish, but there is no data on whether a person fished or not. Some visitors who did fish did not catch any fish so there are excess zeros in the data because of the people that did not fish."
 
 ```{r}
-zinb <- read.csv("http://stats.idre.ucla.edu/stat/data/fish.csv")
+zinb <- read.csv("https://paul-buerkner.github.io/data/fish.csv")
 head(zinb)
 ```