Merge remote-tracking branch 'origin/master'

.github/workflows/tic.yml

@@ -81,6 +81,12 @@ jobs:
           Rscript -e "if (!requireNamespace('remotes')) install.packages('remotes', type = 'source')"
           Rscript -e "if (getRversion() < '3.2' && !requireNamespace('curl')) install.packages('curl', type = 'source')"
 
+      - name: "[Custom block] [Stage] [Linux] Install pkgdown system lib req and valgrind"
+        if: runner.os == 'Linux'
+        run: |
+          sudo apt install libharfbuzz-dev libfribidi-dev
+          sudo apt install -y valgrind
+
       - name: "[Stage] [Linux] Install curl"
         if: runner.os == 'Linux'
         run: sudo apt install libcurl4-openssl-dev

README.Rmd

@@ -24,12 +24,12 @@ set.seed(42)
 
 ## What is survivalmodels?
 
-survivalmodels implements models for survival analysis that are either not already implemented in R, or novel implementations for speed improvements. Currently implemented are five neural networks from the Python packages [pycox](https://github.com/havakv/pycox), DNNSurv,
-and the Akritas non-parametric conditional estimator. Further updates will include implementations of noel survival models.
+`survivalmodels` implements models for survival analysis that are either not already implemented in R, or novel implementations for speed improvements. Currently implemented are five neural networks from the Python packages [pycox](https://github.com/havakv/pycox), DNNSurv,
+and the Akritas non-parametric conditional estimator. Further updates will include implementations of novel survival models.
 
 ## Installation
 
-Install the last release from CRAN:
+Install the latest release from CRAN:
 
 ```{r eval=FALSE}
 install.packages("survivalmodels")

README.md

@@ -20,16 +20,16 @@ MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.or
 
 ## What is survivalmodels?
 
-survivalmodels implements models for survival analysis that are either
+`survivalmodels` implements models for survival analysis that are either
 not already implemented in R, or novel implementations for speed
 improvements. Currently implemented are five neural networks from the
 Python packages [pycox](https://github.com/havakv/pycox), DNNSurv, and
 the Akritas non-parametric conditional estimator. Further updates will
-include implementations of noel survival models.
+include implementations of novel survival models.
 
 ## Installation
 
-Install the last release from CRAN:
+Install the latest release from CRAN:
 
 ``` r
 install.packages("survivalmodels")

tic.R

@@ -2,7 +2,15 @@
 do_package_checks()
 
 if (ci_on_ghactions() && ci_has_env("BUILD_PKGDOWN")) {
+
+get_stage("before_deploy") %>%
+  add_step(step_setup_push_deploy(branch = "master", orphan = FALSE))
+
   # creates pkgdown site and pushes to gh-pages branch
   # only for the runner with the "BUILD_PKGDOWN" env var set
   do_pkgdown()
+
+  get_stage("deploy") %>%
+    add_code_step(rmarkdown::render("README.Rmd")) %>%
+    add_step(step_do_push_deploy(commit_paths = c("README.md")))
 }