documentation and url fixes

DESCRIPTION

@@ -14,5 +14,5 @@ Depends: forestplot (>= 1.6), metafor (>= 2.0-0), numDeriv (>= 2016.8-1.1)
 Suggests: compute.es, knitr, rmarkdown, R.rsp
 Description: A collection of functions allowing to derive the posterior distribution of the two parameters in a random-effects meta-analysis, and providing functionality to evaluate joint and marginal posterior probability distributions, predictive distributions, shrinkage effects, posterior predictive p-values, etc.; For more details, see also Roever C (2020) <doi:10.18637/jss.v093.i06>.
 License: GPL (>=2)
-URL: https://gitlab.gwdg.de/croever/bayesmeta, https://ams.med.uni-goettingen.de:3838/bayesmeta/app
+URL: https://gitlab.gwdg.de/croever/bayesmeta, http://ams.med.uni-goettingen.de:3838/bayesmeta/app
 VignetteBuilder: knitr, R.rsp

man/bayesmeta-package.Rd

@@ -39,7 +39,7 @@ Christian Roever <christian.roever@med.uni-goettingen.de>
   
   C. Roever.
   \emph{The bayesmeta app}.
-  \url{https://ams.med.uni-goettingen.de:3838/bayesmeta/app},
+  \url{http://ams.med.uni-goettingen.de:3838/bayesmeta/app},
   2018.
 }
 \keyword{ models }

man/ess.Rd

@@ -17,7 +17,7 @@
   \item{object}{a \code{bayesmeta} object.}
   \item{uisd}{the \emph{unit infomation standard deviation}
     (a single numerical value, \emph{or} a \code{function} of the
-    parameter (\eqn{\theta}{theta})).}
+    parameter (\eqn{\mu}{mu})).}
   \item{method}{a character string specifying the method to be used for
     ESS computation. By default, the expected local-information-ratio
     ESS (\eqn{ESS_{ELIR}}{ESS_ELIR}) is returned.}
@@ -50,9 +50,9 @@
 
   Specifying the UISD as a constant is often an approximation,
   sometimes it is also possible to specify the UISD as a function of the
-  parameter (\eqn{\theta}{theta}). For example, in case the outcome in
+  parameter (\eqn{\mu}{mu}). For example, in case the outcome in
   the meta-analyses are log-odds, then the UISD varies with the (log-)
-  odds and is given by \eqn{2\,\mathrm{cosh}(\theta/2)}{2*cosh(theta/2)}
+  odds and is given by \eqn{2\,\mathrm{cosh}(\mu/2)}{2*cosh(mu/2)}
   (see also the example below).
 
   The ESS may be computed or approximated in several ways.
@@ -126,7 +126,7 @@ uisdLogOdds <- function(logodds)
 }
 
 # Note: in the present example, probabilities are
-# at approximately 0.25, corresponding to odds of 1/3.
+# at approximately 0.25, corresponding to odds of 1:3.
 uisdLogOdds(log(1/3))
 # The UISD value of 2.31 roughly matches the above empirical figure.