Merge pull request #130 from lindeloev/v0.3.1

Update URLs for CRAN check.

R/mcp-package.R

@@ -22,5 +22,5 @@
 #' * Test hypotheses using `hypothesis()` and `loo()`.
 #'
 #' Extensive documentation with worked examples is available at the
-#' [mcp website](https://lindeloev.github.io/mcp).
+#' [mcp website](https://lindeloev.github.io/mcp/).
 NULL

README.md

@@ -1,7 +1,7 @@
 # mcp: Regression with Multiple Change Points<img src="https://github.com/lindeloev/mcp/raw/docs/man/figures/logo_200px.png" align="right" style="padding: 20px; padding-right: 0px;" />
 
-[![mcp Travis-CI status](https://travis-ci.org/lindeloev/mcp.svg?branch=master)](https://travis-ci.org/lindeloev/mcp)
-[![mcp Coveralls status](https://codecov.io/gh/lindeloev/mcp/branch/master/graph/badge.svg)](https://coveralls.io/r/lindeloev/mcp)
+[![mcp Github Actions status](https://github.com/lindeloev/mcp/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/lindeloev/mcp/actions/workflows/check-standard.yaml)
+[![mcp Coveralls status](https://codecov.io/gh/lindeloev/mcp/branch/master/graph/badge.svg)](https://coveralls.io/github/lindeloev/mcp/)
 [![mcp CRAN status](https://www.r-pkg.org/badges/version/mcp)](https://CRAN.R-project.org/package=mcp)
 [![mcp CRAN downloads](https://cranlogs.r-pkg.org/badges/mcp)](https://cranlogs.r-pkg.org/badges/mcp)
 
@@ -14,7 +14,7 @@ Under the hood, `mcp` takes a formula-representation of linear segments and turn
 
 # Install
 
- 1. <a href="https://sourceforge.net/projects/mcmc-jags/" target="_blank">Install the latest version of JAGS</a>. Linux users can fetch binaries <a href="http://mcmc-jags.sourceforge.net/" target="_blank">here</a>.
+ 1. <a href="https://sourceforge.net/projects/mcmc-jags/" target="_blank">Install the latest version of JAGS</a>. Linux users can fetch binaries <a href="https://mcmc-jags.sourceforge.io/" target="_blank">here</a>.
 
  2. Install from CRAN:
 
@@ -144,7 +144,7 @@ model2 -7.6       4.6
 
 
 # Highlights from in-depth guides
-The articles on the [mcp website](https://lindeloev.github.io/mcp) go in-depth with the functionality of `mcp`. Here is an executive summary, to give you a quick sense of what mcp can do.
+The articles on the [mcp website](https://lindeloev.github.io/mcp/) go in-depth with the functionality of `mcp`. Here is an executive summary, to give you a quick sense of what mcp can do.
 
 [About mcp models and simulating data](https://lindeloev.github.io/mcp/articles/formulas.html):
  * Parameter names are `int_i` (intercepts), `cp_i` (change points), `x_i` (slopes), `phi_i` (autocorrelation), and `sigma_*` (variance).
@@ -187,7 +187,7 @@ Modeling [variance](https://lindeloev.github.io/mcp/articles/variance.html) and
  * `fitted(fit)` and `predict(fit)` take many arguments to predict in-sample and out-of-sample values and intervals.
  * Forecasting with prior knowledge about future change points.
 
-[Tips, tricks, and debugging](https://lindeloev.github.io/mcp/articles/debug.html)
+[Tips, tricks, and debugging](https://lindeloev.github.io/mcp/articles/tips.html)
  * Speed up fitting using `mcp(..., cores = 3)` / `options(mcp_cores = 3)`, and/or fewer iterations, `mcp(..., adapt = 500)`.
  * Help convergence along using `mcp(..., inits = list(cp_1 = 20, int_2 = -3))`.
  * Most errors will be caused by circularly defined priors.

cran-comments.md

@@ -1,3 +1,7 @@
+# Resubmission
+This is a resubmission. I have corrected the invalid URLs in the README.
+
+
 # mcp 0.3.1
 
 ## Notes for the reviewer