add markdown dependency to Suggests

yihui/knitr#1864

.Rbuildignore

@@ -14,3 +14,4 @@ vignettes/cache
 ^cranComments.md$
 ^cran-comments.md$
 ^\.travis\.yml$
+^CRAN-RELEASE$

DESCRIPTION

@@ -5,7 +5,7 @@ Author: Thomas Wutzler
 Maintainer: Thomas Wutzler <[email protected]>
 Description: Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.
 Depends: 
-Suggests: RUnit, knitr, ggplot2, reshape2
+Suggests: RUnit, knitr, markdown, ggplot2, reshape2
 VignetteBuilder: knitr
 License: GPL-2
 LazyData: true

cran-comments.md

@@ -1,9 +1,11 @@
 ## Note
-Added random seeds to tests to avoid accidental failing.
+Added random seeds to tests to avoid accidental failing,
+and markdown dependency to Suggests.
 
 ## Test environments
 * local R 4.0.3 on Mint 20 64bit
 * r-hub.io
+* winbuilder
 
 ## R CMD check results
 There were no ERRORs nor WARNINGs nor NOTEs

vignettes/logitnorm.Rmd

@@ -1,134 +1,134 @@
-<!--
-%\VignetteEngine{knitr}
-%\VignetteIndexEntry{Using logitnorm package)}
--->
-
-```{r setup, include=FALSE}
-library(knitr)
-opts_chunk$set(out.extra='style="display:block; margin: auto"'
-    #, fig.align="center"
-    #, fig.width=4.6, fig.height=3.2
-    , fig.width=6, fig.height=3.75 #goldener Schnitt 1.6
-    , dev.args=list(pointsize=10)
-    , dev=c('png','pdf')
-    )
-knit_hooks$set(spar = function(before, options, envir) {
-    if (before){
-        par( las=1 )                   #also y axis labels horizontal
-        par(mar=c(2.0,3.3,0,0)+0.3 )  #margins
-        par(tck=0.02 )                          #axe-tick length inside plots             
-        par(mgp=c(1.1,0.2,0) )  #positioning of axis title, axis labels, axis
-     }
-})
-library(logitnorm) 
-if( !require(ggplot2) ){
-	print("To generate this vignette, ggplo2 is required.")
-	exit(0)
-}
-library(reshape2)
-# genVigs("logitnorm")
-```
-
-Basic usage
-===============
-
-Distribution
---------------
-
-The logitnormal distribution is useful as a prior density for variables that are
-bounded between 0 and 1, such as proportions. The following figure
-displays its density for various combinations of parameters mu (panels) and
-sigma (lines).
-
-
-```{r densityPlots, spar=TRUE, echo=FALSE, fig.width=6*2, fig.height=3.75*2}
-xGrid =c(0+.Machine$double.eps,seq(0,1, length.out=81)[-c(1,81)],1-.Machine$double.eps)
-#explore chanign sigma at mu=0 
-theta0 <- expand.grid(mu=seq(0,2,length.out=9),
-sigma=10^seq(-0.5,0.5,length.out=5)) 
-n <- nrow(theta0)
-
-.calcDensityGrid <- function(
-		### Calculate lognormal desnity for given combinations
-		theta0	##<< matrix with columns mu and sigma
-		,xGrid = seq(0,1, length.out=81)[-c(1,81)]
-){ 
-	dx <- apply( theta0, 1, function(theta0i){
-				dx <- dlogitnorm(xGrid, mu=theta0i[1], sigma=theta0i[2])
-			})
-	dimnames(dx) <- list(iX=NULL,iTheta=NULL)
-	ds <- melt(dx)	# two variables over time
-	ds[1:10,]
-	ds$mu <- rep(as.factor(round(theta0[,1],2)), each=length(xGrid))
-	ds$sigma <- rep(as.factor(round(theta0[,2],2)), each=length(xGrid))
-	ds$x <- rep(xGrid, nrow(theta0))
-	ds
-	### data frame with columns value,x,mu and sigma 
-}
-
-ds <- .calcDensityGrid(theta0,xGrid=xGrid)
-p1 <- ggplot(ds, aes(x=x, y=value, color=sigma, linetype=sigma)) + geom_line(size=1) +
- facet_wrap(~mu,scales="free")+
- theme_bw() +
- theme(axis.title.x = element_blank()) +  ylab("density") +
- theme()
-print(p1)
-```
-
-Example: Plot the cumulative distribution
-```{r cumDensityPlot, spar=TRUE}
-x <- seq(0,1, length.out=81) 
-d <- plogitnorm(x, mu=0.5, sigma=0.5)
-plot(d~x,type="l")
-```
-
-Mean and Variance
-------------------- 
-The moments have no analytical solution. This package
-estimates them by numerical integration:
-
-Example: estimate mean and standard deviation.
-```{r momentsLogitnorm}
-(theta <- momentsLogitnorm(mu=0.6,sigma=0.5))
-```
-
-Mode
-----
-The mode is found by setting derivatives to zero and optimizing
-the resulting equation: $logit(x) = \sigma^2(2x-1)+\mu$.
-
-Example: estimate the mode
-```{r modeLogitnorm}
-(mle <- modeLogitnorm(mu=0.6,sigma=0.5))
-```
-		
-Parameter Estimation
------------------------
-from upper quantile and 
-  - mode (Maximum Likelihood Estimate)
-  - mean (Expected value)
-  - median
-
-Example: estimate the parameters, with mode 0.7 and upper quantile 0.9
-```{r twCoefLogitnormMLE}
-(theta <- twCoefLogitnormMLE(0.7,0.9))
-x <- seq(0,1, length.out=81) 
-d <- dlogitnorm(x, mu=theta[1,"mu"], sigma=theta[1,"sigma"])
-plot(d~x,type="l")
-abline(v=c(0.7,0.9), col="grey")
-```
-
-When increasing the $\sigma$ parameter, the distribution becomes
-eventually becomes bi-model, i.e. has two maxima. The unimodal distribution for
-a given mode with widest confidence intervals is obtained by
-function `twCoefLogitnormMLEFlat`.
-
-```{r twCoefLogitnormMLEFlat}
-(theta <- twCoefLogitnormMLEFlat(0.7))
-x <- seq(0,1, length.out=81) 
-d <- dlogitnorm(x, mu=theta[1,"mu"], sigma=theta[1,"sigma"])
-plot(d~x,type="l")
-abline(v=c(0.7), col="grey")
-```
-
-
+<!--
+%\VignetteEngine{knitr::knitr}
+%\VignetteIndexEntry{Using logitnorm package)}
+-->
+
+```{r setup, include=FALSE}
+library(knitr)
+opts_chunk$set(out.extra='style="display:block; margin: auto"'
+    #, fig.align="center"
+    #, fig.width=4.6, fig.height=3.2
+    , fig.width=6, fig.height=3.75 #goldener Schnitt 1.6
+    , dev.args=list(pointsize=10)
+    , dev=c('png','pdf')
+    )
+knit_hooks$set(spar = function(before, options, envir) {
+    if (before){
+        par( las=1 )                   #also y axis labels horizontal
+        par(mar=c(2.0,3.3,0,0)+0.3 )  #margins
+        par(tck=0.02 )                          #axe-tick length inside plots             
+        par(mgp=c(1.1,0.2,0) )  #positioning of axis title, axis labels, axis
+     }
+})
+library(logitnorm) 
+if( !require(ggplot2) ){
+	print("To generate this vignette, ggplo2 is required.")
+	exit(0)
+}
+library(reshape2)
+# genVigs("logitnorm")
+```
+
+Basic usage
+===============
+
+Distribution
+--------------
+
+The logitnormal distribution is useful as a prior density for variables that are
+bounded between 0 and 1, such as proportions. The following figure
+displays its density for various combinations of parameters mu (panels) and
+sigma (lines).
+
+
+```{r densityPlots, spar=TRUE, echo=FALSE, fig.width=6*2, fig.height=3.75*2}
+xGrid =c(0+.Machine$double.eps,seq(0,1, length.out=81)[-c(1,81)],1-.Machine$double.eps)
+#explore chanign sigma at mu=0 
+theta0 <- expand.grid(mu=seq(0,2,length.out=9),
+sigma=10^seq(-0.5,0.5,length.out=5)) 
+n <- nrow(theta0)
+
+.calcDensityGrid <- function(
+		### Calculate lognormal desnity for given combinations
+		theta0	##<< matrix with columns mu and sigma
+		,xGrid = seq(0,1, length.out=81)[-c(1,81)]
+){ 
+	dx <- apply( theta0, 1, function(theta0i){
+				dx <- dlogitnorm(xGrid, mu=theta0i[1], sigma=theta0i[2])
+			})
+	dimnames(dx) <- list(iX=NULL,iTheta=NULL)
+	ds <- melt(dx)	# two variables over time
+	ds[1:10,]
+	ds$mu <- rep(as.factor(round(theta0[,1],2)), each=length(xGrid))
+	ds$sigma <- rep(as.factor(round(theta0[,2],2)), each=length(xGrid))
+	ds$x <- rep(xGrid, nrow(theta0))
+	ds
+	### data frame with columns value,x,mu and sigma 
+}
+
+ds <- .calcDensityGrid(theta0,xGrid=xGrid)
+p1 <- ggplot(ds, aes(x=x, y=value, color=sigma, linetype=sigma)) + geom_line(size=1) +
+ facet_wrap(~mu,scales="free")+
+ theme_bw() +
+ theme(axis.title.x = element_blank()) +  ylab("density") +
+ theme()
+print(p1)
+```
+
+Example: Plot the cumulative distribution
+```{r cumDensityPlot, spar=TRUE}
+x <- seq(0,1, length.out=81) 
+d <- plogitnorm(x, mu=0.5, sigma=0.5)
+plot(d~x,type="l")
+```
+
+Mean and Variance
+------------------- 
+The moments have no analytical solution. This package
+estimates them by numerical integration:
+
+Example: estimate mean and standard deviation.
+```{r momentsLogitnorm}
+(theta <- momentsLogitnorm(mu=0.6,sigma=0.5))
+```
+
+Mode
+----
+The mode is found by setting derivatives to zero and optimizing
+the resulting equation: $logit(x) = \sigma^2(2x-1)+\mu$.
+
+Example: estimate the mode
+```{r modeLogitnorm}
+(mle <- modeLogitnorm(mu=0.6,sigma=0.5))
+```
+		
+Parameter Estimation
+-----------------------
+from upper quantile and 
+  - mode (Maximum Likelihood Estimate)
+  - mean (Expected value)
+  - median
+
+Example: estimate the parameters, with mode 0.7 and upper quantile 0.9
+```{r twCoefLogitnormMLE}
+(theta <- twCoefLogitnormMLE(0.7,0.9))
+x <- seq(0,1, length.out=81) 
+d <- dlogitnorm(x, mu=theta[1,"mu"], sigma=theta[1,"sigma"])
+plot(d~x,type="l")
+abline(v=c(0.7,0.9), col="grey")
+```
+
+When increasing the $\sigma$ parameter, the distribution becomes
+eventually becomes bi-model, i.e. has two maxima. The unimodal distribution for
+a given mode with widest confidence intervals is obtained by
+function `twCoefLogitnormMLEFlat`.
+
+```{r twCoefLogitnormMLEFlat}
+(theta <- twCoefLogitnormMLEFlat(0.7))
+x <- seq(0,1, length.out=81) 
+d <- dlogitnorm(x, mu=theta[1,"mu"], sigma=theta[1,"sigma"])
+plot(d~x,type="l")
+abline(v=c(0.7), col="grey")
+```
+
+