use https for mc-stan.org links in vignettes

Author: jgabry

vignettes/graphical-ppcs.Rmd

@@ -25,9 +25,9 @@ library("rstanarm")
 
 This vignette focuses on graphical posterior predictive checks (PPC). Plots of parameter estimates
 from MCMC draws are covered in the separate vignette
-[_Plotting MCMC draws_](http://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html),
+[_Plotting MCMC draws_](https://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html),
 and MCMC diagnostics are covered in the
-[_Visual MCMC diagnostics_](http://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html)
+[_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html)
 vignette.
 
 ### Graphical posterior predictive checks (PPCs)
@@ -89,7 +89,7 @@ library("rstanarm")
 To demonstrate some of the various PPCs that can be created with the
 **bayesplot** package we'll use an example of comparing Poisson and Negative
 binomial regression models from one of the
-**rstanarm** [package vignettes](http://mc-stan.org/rstanarm/articles/count.html) 
+**rstanarm** [package vignettes](https://mc-stan.org/rstanarm/articles/count.html) 
 (Gabry and Goodrich, 2017).
 
 > We want to make inferences about the efficacy of a certain pest management system at reducing the number of roaches in urban apartments. [...] The regression predictors for the model are the pre-treatment number of roaches `roach1`, the treatment indicator `treatment`, and a variable `senior` indicating whether the apartment is in a building restricted to elderly residents. Because the number of days for which the roach traps were used is not the same for all apartments in the sample, we include it as an exposure [...]. 
@@ -269,7 +269,7 @@ See Figure 7 in [Gabry et al. (2019)](#gabry2019) for another example of using
 There are many additional PPCs available, including plots of predictive 
 intervals, distributions of predictive errors, and more. For links to the
 documentation for all of the various PPC plots see `help("PPC-overview")`
-from R or the [online documentation](http://mc-stan.org/bayesplot/reference/index.html#section-ppc) on the Stan website. 
+from R or the [online documentation](https://mc-stan.org/bayesplot/reference/index.html#section-ppc) on the Stan website. 
 
 The `available_ppc` function can also be used to list the names of all PPC
 plotting functions:
@@ -381,7 +381,7 @@ version 1.7.0. https://CRAN.R-project.org/package=brms
 
 Gabry, J., and Goodrich, B. (2017). rstanarm: Bayesian Applied Regression 
 Modeling via Stan. R package version 2.15.3. 
-http://mc-stan.org/rstanarm, https://CRAN.R-project.org/package=rstanarm
+https://mc-stan.org/rstanarm, https://CRAN.R-project.org/package=rstanarm
 
 Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), 
 Visualization in Bayesian workflow. _J. R. Stat. Soc. A_, 182: 389-402. 
@@ -394,4 +394,4 @@ D. B. (2013). *Bayesian Data Analysis*. Chapman & Hall/CRC Press, London, third
 edition.
 
 Stan Development Team. (2017). *Stan Modeling Language Users
-Guide and Reference Manual*. http://mc-stan.org/users/documentation/
+Guide and Reference Manual*. https://mc-stan.org/users/documentation/

vignettes/plotting-mcmc-draws.Rmd

@@ -26,9 +26,9 @@ library("rstanarm")
 
 This vignette focuses on plotting parameter estimates from MCMC draws. MCMC 
 diagnostic plots are covered in the separate vignette
-[_Visual MCMC diagnostics_](http://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html), 
+[_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html), 
 and graphical posterior predictive model checking is covered in the vignette
-[_Graphical posterior predictive checks_](http://mc-stan.org/bayesplot/articles/graphical-ppcs.html).
+[_Graphical posterior predictive checks_](https://mc-stan.org/bayesplot/articles/graphical-ppcs.html).
 
 ### Setup
 
@@ -53,7 +53,7 @@ In this vignette we'll use draws obtained using the `stan_glm` function in the
 **rstanarm** package (Gabry and Goodrich, 2017), but MCMC draws from using 
 any package can be used with the functions in the **bayesplot** package. See,
 for example, **brms**, which, like **rstanarm**, calls the **rstan** package
-internally to use [Stan](http://mc-stan.org/)'s MCMC sampler.
+internally to use [Stan](https://mc-stan.org/)'s MCMC sampler.
 
 ```{r, mtcars}
 head(mtcars) # see help("mtcars")
@@ -102,7 +102,7 @@ particular cases).
 **Documentation:**
 
 * `help("MCMC-intervals")` 
-* [mc-stan.org/bayesplot/reference/MCMC-intervals](http://mc-stan.org/bayesplot/reference/MCMC-intervals.html)
+* [mc-stan.org/bayesplot/reference/MCMC-intervals](https://mc-stan.org/bayesplot/reference/MCMC-intervals.html)
 
 ------
 
@@ -147,7 +147,7 @@ combined or with the chains separate.
 **Documentation:**
 
 * `help("MCMC-distributions")` 
-* [mc-stan.org/bayesplot/reference/MCMC-distributions](http://mc-stan.org/bayesplot/reference/MCMC-distributions.html)
+* [mc-stan.org/bayesplot/reference/MCMC-distributions](https://mc-stan.org/bayesplot/reference/MCMC-distributions.html)
 
 ------
 
@@ -224,13 +224,13 @@ mcmc_violin(posterior, pars = c("wt", "sigma"), probs = c(0.1, 0.5, 0.9))
 Various functions are available for plotting bivariate marginal posterior 
 distributions. Some of these functions also take optional arguments 
 for adding MCMC diagnostic information to the plots. That additional functionality 
-is discussed in the separate [_Visual MCMC diagnostics_](http://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html)
+is discussed in the separate [_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html)
 vignette.
 
 **Documentation:**
 
 * `help("MCMC-scatterplots")` 
-* [mc-stan.org/bayesplot/reference/MCMC-scatterplots](http://mc-stan.org/bayesplot/reference/MCMC-scatterplots.html)
+* [mc-stan.org/bayesplot/reference/MCMC-scatterplots](https://mc-stan.org/bayesplot/reference/MCMC-scatterplots.html)
 
 ------
 
@@ -273,7 +273,7 @@ above the diagonal and the others below. There are many more options for
 controlling how the draws should be split between the plots above and below the
 diagonal (see the documentation for the `condition` argument), but they are more
 useful when MCMC diagnostic information is included. This is discussed in the 
-[_Visual MCMC diagnostics_](http://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html)
+[_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html)
 vignette.
 
 
@@ -284,14 +284,14 @@ vignette.
 Trace plots are time series plots of Markov chains. In this vignette 
 we show the standard trace plots that **bayesplot** can make. For models
 fit using any Stan interface (or Hamiltonian Monte Carlo in general), the 
-[_Visual MCMC diagnostics_](http://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html)
+[_Visual MCMC diagnostics_](https://mc-stan.org/bayesplot/articles/visual-mcmc-diagnostics.html)
 vignette provides an example of also adding information about divergences
 to trace plots.
 
 **Documentation:**
 
 * `help("MCMC-traces")` 
-* [mc-stan.org/bayesplot/reference/MCMC-traces](http://mc-stan.org/bayesplot/reference/MCMC-traces.html)
+* [mc-stan.org/bayesplot/reference/MCMC-traces](https://mc-stan.org/bayesplot/reference/MCMC-traces.html)
 
 -------
 
@@ -344,7 +344,7 @@ mcmc_trace_highlight(posterior, pars = "sigma", highlight = 3)
 
 Gabry, J., and Goodrich, B. (2017). rstanarm: Bayesian Applied Regression 
 Modeling via Stan. R package version 2.15.3. 
-http://mc-stan.org/rstanarm, https://CRAN.R-project.org/package=rstanarm
+https://mc-stan.org/rstanarm, https://CRAN.R-project.org/package=rstanarm
 
 Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), 
 Visualization in Bayesian workflow. _J. R. Stat. Soc. A_, 182: 389-402. 
@@ -356,4 +356,4 @@ Rubin, D. B. (2013). *Bayesian Data Analysis*. Chapman & Hall/CRC Press, London,
 third edition.
 
 Stan Development Team. (2017). *Stan Modeling Language Users
-Guide and Reference Manual*. http://mc-stan.org/users/documentation/
+Guide and Reference Manual*. https://mc-stan.org/users/documentation/

vignettes/visual-mcmc-diagnostics.Rmd

@@ -31,13 +31,13 @@ This vignette focuses on MCMC diagnostic plots, in particular on diagnosing
 divergent transitions and on the `n_eff` and `Rhat` statistics that help you
 determine that the chains have mixed well. Plots of parameter estimates
 from MCMC draws are covered in the separate vignette
-[_Plotting MCMC draws_](http://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html), 
+[_Plotting MCMC draws_](https://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html), 
 and graphical posterior predictive model checking is covered in the
-[_Graphical posterior predictive checks_](http://mc-stan.org/bayesplot/articles/graphical-ppcs.html)
+[_Graphical posterior predictive checks_](https://mc-stan.org/bayesplot/articles/graphical-ppcs.html)
 vignette.
 
 Note that most of these plots can also be browsed interactively using the 
-[shinystan](http://mc-stan.org/shinystan/) package.
+[shinystan](https://mc-stan.org/shinystan/) package.
 
 ### Setup
 
@@ -168,7 +168,7 @@ You may have noticed the warnings about divergent transitions for the centered
 parametrization fit. Those are serious business and in most cases indicate that
 something is wrong with the model and the results should not be trusted. 
 For an explanation of these warnings see 
-[Divergent transitions after warmup](http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup).
+[Divergent transitions after warmup](https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup).
 We'll have a look at diagnosing the source of the divergences first and then
 dive into some diagnostics that should be checked even if there are no warnigns
 from the sampler.
@@ -178,7 +178,7 @@ from the sampler.
 ## Diagnostics for the No-U-Turn Sampler
 
 The No-U-Turn Sampler (NUTS, Hoffman and Gelman, 2014) is the variant of 
-Hamiltonian Monte Carlo (HMC) used by [Stan](http://mc-stan.org/) and the
+Hamiltonian Monte Carlo (HMC) used by [Stan](https://mc-stan.org/) and the
 various R packages that depend on Stan for fitting Bayesian models.
 The **bayesplot** package has special functions for visualizing some of the 
 unique diagnostics permitted by HMC, and NUTS in particular. See Betancourt 
@@ -188,7 +188,7 @@ more details on the concepts.
 **Documentation:**
 
 * `help("MCMC-nuts")` 
-* [mc-stan.org/bayesplot/reference/MCMC-nuts](http://mc-stan.org/bayesplot/reference/MCMC-nuts.html)
+* [mc-stan.org/bayesplot/reference/MCMC-nuts](https://mc-stan.org/bayesplot/reference/MCMC-nuts.html)
 
 ------
 
@@ -239,7 +239,7 @@ if(n_divergent_ncp > 0) {
 
 In addition to the NUTS-specific plotting functions, some of the general 
 MCMC plotting functions demonstrated in the 
-[_Plotting MCMC draws_](http://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html)
+[_Plotting MCMC draws_](https://mc-stan.org/bayesplot/articles/plotting-mcmc-draws.html)
 vignette also take optional arguments that can be used to display important
 HMC/NUTS diagnostic information. We'll see examples of this in the next
 section on divergenent transitions.
@@ -574,13 +574,13 @@ chain(s) have converged. The __bayesplot__ package provides various plotting
 functions for visualizing Markov chain Monte Carlo (MCMC) diagnostics after 
 fitting a Bayesian model. MCMC draws from any package can be used, although
 there are a few diagnostic plots that we will see later in this vignette that
-are specifically intended to be used for [Stan](http://mc-stan.org/) models (or
+are specifically intended to be used for [Stan](https://mc-stan.org/) models (or
 models fit using the same algorithms as Stan).
 
 **Documentation:**
 
 * `help("MCMC-diagnostics")` 
-* [mc-stan.org/bayesplot/reference/MCMC-diagnostics](http://mc-stan.org/bayesplot/reference/MCMC-diagnostics.html)
+* [mc-stan.org/bayesplot/reference/MCMC-diagnostics](https://mc-stan.org/bayesplot/reference/MCMC-diagnostics.html)
 
 ------
 
@@ -766,7 +766,7 @@ models. https://arxiv.org/abs/1312.0906
 
 Gabry, J., and Goodrich, B. (2018). rstanarm: Bayesian Applied Regression
 Modeling via Stan. R package version 2.17.4.
-http://mc-stan.org/rstanarm
+https://mc-stan.org/rstanarm
 
 Gabry, J. , Simpson, D. , Vehtari, A. , Betancourt, M. and Gelman, A. (2019), 
 Visualization in Bayesian workflow. _J. R. Stat. Soc. A_, 182: 389-402. 
@@ -789,7 +789,7 @@ Rubin, D. B. (1981). Estimation in Parallel Randomized Experiments. *Journal of
 Educational and Behavioral Statistics*. 6:377--401.
 
 Stan Development Team. (2018). *Stan Modeling Language Users
-Guide and Reference Manual*. http://mc-stan.org/users/documentation/
+Guide and Reference Manual*. https://mc-stan.org/users/documentation/
 
 Stan Development Team. (2018). RStan: the R interface to Stan. R package version 2.17.3.
-http://mc-stan.org/rstan
+https://mc-stan.org/rstan