Makes README.Rmd so I can use programmatically variables in the resulting README.md that can be built using devtools::build_readme()

Author: giabaio

.Rbuildignore

@@ -23,3 +23,7 @@
 ^CRAN-SUBMISSION$
 ^CITATION.cff
 ^\.github$
+^README\.Rmd$
+^_README_cache$
+^README_files$
+^README\.md$

README.Rmd

@@ -0,0 +1,54 @@
+---
+title: "survHE"
+format: gfm
+execute:
+  echo: false
+  message: false
+  warning: false
+output: github_document
+---
+
+[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/survHE)](https://cran.r-project.org/package=survHE)
+[![CRAN_Download_Badge](https://cranlogs.r-pkg.org/badges/survHE)](https://cran.r-project.org/package=survHE)
+[![CRAN_Download_Badge](https://cranlogs.r-pkg.org:443/badges/grand-total/survHE?color=orange)](https://cranlogs.r-pkg.org:443/badges/grand-total/survHE?color=orange)
+
+## Survival analysis in health economic evaluation
+
+:rocket: This is the **development version** of the `R` package `survHE` (currently at version `r packageDescription("survHE")$Version`). A "stable" version (as of `r Sys.Date() |> format("%-d %B %Y")`: `r utils::available.packages(filters = "CRAN")["survHE",2]`) is packaged and available from [CRAN](https://cran.r-project.org/web/packages/survHE/index.html).
+
+Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package [flexsurv](https://CRAN.R-project.org/package=flexsurv)) and a Bayesian perspective. For a selected range of models, both Integrated Nested Laplace Integration (via the R package [INLA](https://www.r-inla.org/)) and Hamiltonian Monte Carlo (via the R package [rstan](https://CRAN.R-project.org/package=rstan)) are possible. HMC models are pre-compiled so that they can run in a very efficient and fast way. In addition to model fitting, survHE provides a set of specialised functions, for example to perform Probabilistic Sensitivity Analysis, export the results of the modelling to a spreadsheet, plotting survival curves and uncertainty around the mean estimates.
+
+**NB**: To run the Bayesian models, as of version 2.0 of `survHE`, it is necessary to install the additional packages [`survHEinla`](https://github.com/giabaio/survHEinla) and/or [`survHEhmc`](https://github.com/giabaio/survHEhmc), which are available from this GitHub repository. The reason for this structural change is that in this way, the basic backbone of `survHE` (available from this `main` branch of the repo) becomes a very lean package, whose installation is very quick. More details [here](https://gianluca.statistica.it/blog/2022-01-18-survhe-light/). All the functionalities are in place for `survHE` to easily extend to the Bayesian versions, once one or both of the additional "modules" is also installed.
+
+## Installation
+The most updated version can be installed using the following code.
+
+```R
+install.packages(
+   "survHE", 
+   repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org")
+)
+```
+
+To run the Bayesian versions of the models, you also need to install the ancillary packages
+
+```R
+# Bayesian models using HMC/Stan
+install.packages(
+   "survHEhmc", 
+   repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org"),
+   dependencies=TRUE
+)
+
+# Bayesian models using INLA
+install.packages(
+   "survHEinla", 
+   repos = c(
+      "https://giabaio.r-universe.dev", 
+      "https://cloud.r-project.org",
+      "https://inla.r-inla-download.org/R/stable"
+   ),
+   dependencies=TRUE
+)
+```
+(these two are optional, in some sense, so you don't *have* to, unless you want to do the right thing and be Bayesian about it... :wink:)

README.md

@@ -1,28 +1,59 @@
-# survHE 
+survHE
+================
 
 [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/survHE)](https://cran.r-project.org/package=survHE)
 [![CRAN_Download_Badge](https://cranlogs.r-pkg.org/badges/survHE)](https://cran.r-project.org/package=survHE)
 [![CRAN_Download_Badge](https://cranlogs.r-pkg.org:443/badges/grand-total/survHE?color=orange)](https://cranlogs.r-pkg.org:443/badges/grand-total/survHE?color=orange)
 
 ## Survival analysis in health economic evaluation
 
-:rocket: This is the **development version** of the `R` package `survHE`. The stable version is now release 2.0.5, on [CRAN](https://cran.r-project.org/web/packages/survHE/index.html).
+:rocket: This is the **development version** of the `R` package `survHE`
+(currently at version 2.0.51). A “stable” version (as of 11 July 2025:
+2.0.5) is packaged and available from
+[CRAN](https://cran.r-project.org/web/packages/survHE/index.html).
 
-Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package [flexsurv](https://CRAN.R-project.org/package=flexsurv)) and a Bayesian perspective. For a selected range of models, both Integrated Nested Laplace Integration (via the R package [INLA](https://www.r-inla.org/)) and Hamiltonian Monte Carlo (via the R package [rstan](https://CRAN.R-project.org/package=rstan)) are possible. HMC models are pre-compiled so that they can run in a very efficient and fast way. In addition to model fitting, survHE provides a set of specialised functions, for example to perform Probabilistic Sensitivity Analysis, export the results of the modelling to a spreadsheet, plotting survival curves and uncertainty around the mean estimates.
+Contains a suite of functions to systematise the workflow involving
+survival analysis in health economic evaluation. survHE can fit a large
+range of survival models using both a frequentist approach (by calling
+the R package [flexsurv](https://CRAN.R-project.org/package=flexsurv))
+and a Bayesian perspective. For a selected range of models, both
+Integrated Nested Laplace Integration (via the R package
+[INLA](https://www.r-inla.org/)) and Hamiltonian Monte Carlo (via the R
+package [rstan](https://CRAN.R-project.org/package=rstan)) are possible.
+HMC models are pre-compiled so that they can run in a very efficient and
+fast way. In addition to model fitting, survHE provides a set of
+specialised functions, for example to perform Probabilistic Sensitivity
+Analysis, export the results of the modelling to a spreadsheet, plotting
+survival curves and uncertainty around the mean estimates.
 
-**NB**: To run the Bayesian models, as of version 2.0 of `survHE`, it is necessary to install the additional packages [`survHEinla`](https://github.com/giabaio/survHEinla) and/or [`survHEhmc`](https://github.com/giabaio/survHEhmc), which are available from this GitHub repository. The reason for this structural change is that in this way, the basic backbone of `survHE` (available from this `main` branch of the repo) becomes a very lean package, whose installation is very quick. More details [here](https://gianluca.statistica.it/blog/2022-01-18-survhe-light/). All the functionalities are in place for `survHE` to easily extend to the Bayesian versions, once one or both of the additional "modules" is also installed.
+**NB**: To run the Bayesian models, as of version 2.0 of `survHE`, it is
+necessary to install the additional packages
+[`survHEinla`](https://github.com/giabaio/survHEinla) and/or
+[`survHEhmc`](https://github.com/giabaio/survHEhmc), which are available
+from this GitHub repository. The reason for this structural change is
+that in this way, the basic backbone of `survHE` (available from this
+`main` branch of the repo) becomes a very lean package, whose
+installation is very quick. More details
+[here](https://gianluca.statistica.it/blog/2022-01-18-survhe-light/).
+All the functionalities are in place for `survHE` to easily extend to
+the Bayesian versions, once one or both of the additional “modules” is
+also installed.
 
 ## Installation
+
 The most updated version can be installed using the following code.
-```R
+
+``` r
 install.packages(
    "survHE", 
    repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org")
 )
 ```
 
-To run the Bayesian versions of the models, you also need to install the ancillary packages
-```R
+To run the Bayesian versions of the models, you also need to install the
+ancillary packages
+
+``` r
 # Bayesian models using HMC/Stan
 install.packages(
    "survHEhmc", 
@@ -41,4 +72,6 @@ install.packages(
    dependencies=TRUE
 )
 ```
-(these two are optional, in some sense, so you don't *have* to, unless you want to do the right thing and be Bayesian about it... :wink:)
+
+(these two are optional, in some sense, so you don’t *have* to, unless
+you want to do the right thing and be Bayesian about it… :wink:)