fix cran badge

README.Rmd

@@ -27,7 +27,7 @@ knitr::opts_chunk$set(
 [![Codecov test coverage](https://codecov.io/gh/ropensci/dynamite/branch/main/graph/badge.svg)](https://app.codecov.io/gh/ropensci/dynamite?branch=main)
 [![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/554_status.svg)](https://github.com/ropensci/software-review/issues/554)
 [![dynamite status badge](https://ropensci.r-universe.dev/badges/dynamite)](https://ropensci.r-universe.dev)
-[![dynamite CRAN badge](http://www.r-pkg.org/badges/version/dynamite)](https://cran.r-project.org/package=dynamite)
+[![dynamite CRAN badge](https://www.r-pkg.org/badges/version/dynamite)](https://cran.r-project.org/package=dynamite)
 <!-- badges: end -->
 
 The `dynamite` [R](https://www.r-project.org/) package provides an easy-to-use interface for Bayesian inference of complex panel (time series) data comprising of multiple measurements per multiple individuals measured in time via dynamic multivariate panel models (DMPM). The main features distinguishing the package and the underlying methodology from many other approaches are:

README.md

@@ -16,7 +16,7 @@ Review](https://badges.ropensci.org/554_status.svg)](https://github.com/ropensci
 [![dynamite status
 badge](https://ropensci.r-universe.dev/badges/dynamite)](https://ropensci.r-universe.dev)
 [![dynamite CRAN
-badge](http://www.r-pkg.org/badges/version/dynamite)](https://cran.r-project.org/package=dynamite)
+badge](https://www.r-pkg.org/badges/version/dynamite)](https://cran.r-project.org/package=dynamite)
 <!-- badges: end -->
 
 The `dynamite` [R](https://www.r-project.org/) package provides an
@@ -109,25 +109,25 @@ print(gaussian_example_fit)
 #> 
 #> No divergences, saturated max treedepths or low E-BFMIs.
 #> 
-#> Smallest bulk-ESS: 661 (sigma_nu_y_alpha)
-#> Smallest tail-ESS: 1058 (sigma_nu_y_alpha)
-#> Largest Rhat: 1.003 (sigma_y)
+#> Smallest bulk-ESS: 651 (sigma_nu_y_alpha)
+#> Smallest tail-ESS: 853 (sigma_nu_y_alpha)
+#> Largest Rhat: 1.002 (beta_y_z)
 #> 
 #> Elapsed time (seconds):
 #>         warmup sample
-#> chain:1  5.824  3.531
-#> chain:2  5.669  3.612
+#> chain:1 10.157  6.377
+#> chain:2 10.431  5.864
 #> 
 #> Summary statistics of the time- and group-invariant parameters:
 #> # A tibble: 6 × 10
 #>   variable      mean median      sd     mad     q5   q95  rhat ess_bulk ess_tail
 #>   <chr>        <dbl>  <dbl>   <dbl>   <dbl>  <dbl> <dbl> <dbl>    <dbl>    <dbl>
-#> 1 beta_y_z    1.97   1.97   0.0116  0.0112  1.95   1.99   1.00    2815.    1434.
-#> 2 sigma_nu_y… 0.0944 0.0933 0.0114  0.0107  0.0780 0.114  1.00     661.    1058.
-#> 3 sigma_y     0.198  0.198  0.00373 0.00362 0.192  0.204  1.00    2580.    1254.
-#> 4 tau_alpha_y 0.212  0.205  0.0483  0.0432  0.146  0.301  1.00    1731.    1606.
-#> 5 tau_y_x     0.364  0.355  0.0740  0.0648  0.266  0.494  1.00    2812.    1504.
-#> 6 tau_y_y_la… 0.107  0.105  0.0219  0.0213  0.0781 0.148  1.00    2387.    1682.
+#> 1 beta_y_z    1.97   1.97   0.0118  0.0119  1.95   1.99  1.00     3946.    1727.
+#> 2 sigma_nu_y… 0.0946 0.0938 0.0116  0.0108  0.0772 0.115 1.00      651.     853.
+#> 3 sigma_y     0.198  0.198  0.00371 0.00378 0.192  0.204 1.00     2734.    1459.
+#> 4 tau_alpha_y 0.213  0.207  0.0481  0.0456  0.146  0.302 1.00     1797.    1634.
+#> 5 tau_y_x     0.362  0.352  0.0683  0.0607  0.269  0.488 1.00     2600.    1492.
+#> 6 tau_y_y_la… 0.107  0.104  0.0204  0.0193  0.0777 0.144 0.999    2118.    1899.
 ```
 
 Posterior estimates of time-varying effects: