Fix more URLs in the README

README.md

@@ -1,9 +1,9 @@
 # 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/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)
+[![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/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/)
 
 `mcp` does regression with one or Multiple Change Points (MCP) between Generalized and hierarchical Linear Segments using Bayesian inference. `mcp` aims to provide maximum flexibility for analyses with a priori knowledge about the number of change points and the form of the segments in between.
 
@@ -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.io/" 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.