19: Make ready for release

.Rbuildignore

@@ -17,3 +17,6 @@ coverage.*
 init.sh
 workflows.md
 images
+^README\.Rmd$
+^data-raw$
+^node_modules$

.pre-commit-config.yaml

@@ -57,6 +57,10 @@ repos:
     rev: v4.0.0-alpha.8
     hooks:
       - id: prettier
+        exclude: >
+          (?x)^(
+          README.md
+          )$
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.5.0
     hooks:
@@ -65,7 +69,11 @@ repos:
       - id: end-of-file-fixer
         exclude: '\.Rd'
       - id: trailing-whitespace
-        exclude: '\.Rd'
+        exclude: >
+          (?x)^(
+          .*\.Rd|
+          README.md
+          )$
       - id: check-yaml
       - id: no-commit-to-branch
       - id: mixed-line-ending
@@ -87,3 +95,8 @@ repos:
     rev: v0.39.0
     hooks:
       - id: markdownlint
+        exclude: >
+          (?x)^(
+          README.md|
+          NEWS.md
+          )$

DESCRIPTION

@@ -7,24 +7,25 @@ Authors@R: c(
     person("Thomas", "Kuenzel", email = "thomas.kuenzel@roche.com", role = c("aut", "cre")),
     person("Kaspar", "Rufibach", email = "kaspar.rufibach@roche.com", role = "aut"),
     person("Regina", "Stegherr", email = "regina.stegherr@uni-ulm.de", role = "aut"),
-    person("Daniel", "Sabanes Bove", email = "daniel.sabanes_bove@roche.com", role = "aut"),
+    person("Daniel", "Sabanés Bové", email = "daniel.sabanes_bove@roche.com", role = "aut"),
     person("F. Hoffmann-La Roche AG", role = c("cph", "fnd")),
     person("Universität Ulm", role = c("cph", "fnd"))
     )
 Description: The SAVVY project is a consortium of academic and pharmaceutical
     industry partners that aims to improve the analyses of adverse event (AE)
     data in clinical trials through the use of survival techniques appropriately
-    dealing with varying follow-up times and competing events.
-    Although statistical methodologies have advanced, in AE analyses often
-    the incidence proportion, the incidence density or a non-parametric
-    Kaplan-Meier estimator are used, which either ignore censoring or
+    dealing with varying follow-up times and competing events, see
+    Stegherr, Schmoor, Beyersmann, et al. (2021) <doi:10.1186/s13063-021-05354-x>.
+    Although statistical methodologies have advanced,
+    in AE analyses often the incidence proportion, the incidence density or a
+    non-parametric Kaplan-Meier estimator are used, which either ignore censoring or
     competing events. This package contains functions to easily conduct the
     proposed improved AE analyses.
 License: Apache License 2.0
-URL: https://github.com/openpharma/savvyr/
+URL: https://openpharma.github.io/savvyr/
 BugReports: https://github.com/openpharma/savvyr/issues
 Depends:
-    R (>= 3.6)
+    R (>= 4.0)
 Imports:
     checkmate,
     etm,
@@ -33,15 +34,10 @@ Suggests:
     kableExtra,
     knitr,
     rmarkdown,
-    testthat (>= 2.0),
-    roxytypes,
-    roxylint
-Remotes:
-    openpharma/roxytypes,
-    openpharma/roxylint,
+    testthat (>= 3.0)
 Config/Needs/documentation:
-    roxytypes,
-    roxylint
+    roxytypes=github::openpharma/roxytypes,
+    roxylint=github::openpharma/roxylint
 RdMacros:
     Rdpack
 VignetteBuilder:

NEWS.md

@@ -1,13 +1,9 @@
 # savvyr 0.1.0
 
-### New features
+- First CRAN version of the package.
+- The package provides functions to easily conduct the improved AE analyses proposed by the SAVVY framework.
 
-* Add an initializer script.
+### New Features
 
-### Enhancements
-
-* Documentation on how to use the initialize a package.
-
-### Bug fixes
-
-* None.
+- Estimators that do not account for competing events (incidence proportion, incidence density, Inverse Kaplan Meier).
+- Estimators accounting for competing events (incidence proportion accounting for competing events and Aalen-Johansen, both first with death only as hard competing event, or using all competing events).

README.Rmd

@@ -0,0 +1,64 @@
+---
+output: github_document
+---
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  fig.path = "man/figures/README-",
+  out.width = "100%"
+)
+```
+
+# savvyr <img src="man/figures/logo.png" align="right" width="175" />
+
+<!-- badges: start -->
+[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
+[![CRAN status](https://www.r-pkg.org/badges/version-last-release/savvyr)](https://www.r-pkg.org/badges/version-last-release/savvyr)
+[![CRAN monthly downloads](https://cranlogs.r-pkg.org/badges/savvyr)](https://cranlogs.r-pkg.org/badges/savvyr)
+[![CRAN total downloads](https://cranlogs.r-pkg.org/badges/grand-total/savvyr)](https://cranlogs.r-pkg.org/badges/grand-total/savvyr)
+[![Code Coverage](https://raw.githubusercontent.com/openpharma/savvyr/_xml_coverage_reports/data/main/badge.svg)](https://raw.githubusercontent.com/openpharma/savvyr/_xml_coverage_reports/data/main/coverage.xml)
+<!-- badges: end -->
+\
+
+The SAVVY project is a consortium of academic and pharmaceutical
+industry partners that aims to improve the analyses of adverse event (AE)
+data in clinical trials through the use of survival techniques appropriately
+dealing with varying follow-up times and competing events.
+Although statistical methodologies have advanced, in AE analyses often
+the incidence proportion, the incidence density or a non-parametric
+Kaplan-Meier estimator are used, which either ignore censoring or
+competing events. The `savvyr` package contains functions to easily conduct the
+proposed improved AE analyses.
+
+## Installation
+
+### Release
+
+You can install the current release version from *CRAN* with:
+
+```{r cran-installation, eval = FALSE}
+install.packages("savvyr")
+```
+
+### Development
+
+You can install the development version of savvyr from [GitHub](https://github.com/) with:
+
+``` r
+if (!require("remotes")) {
+  install.packages("remotes")
+}
+remotes::install_github("openpharma/savvyr")
+```
+
+## Getting Started
+
+See the [introductory vignette](https://openpharma.github.io/savvyr/main/articles/introduction.html) to get started.
+
+## Citing `savvyr`
+
+To cite `savvyr` please see [here](https://openpharma.github.io/savvyr/main/authors.html#citation).

README.md

@@ -1,3 +1,62 @@
-# savvyr
 
-Short description of the package
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+# savvyr <img src="man/figures/logo.png" align="right" width="175" />
+
+<!-- badges: start -->
+
+[![Project Status: Active – The project has reached a stable, usable
+state and is being actively
+developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
+[![CRAN
+status](https://www.r-pkg.org/badges/version-last-release/savvyr)](https://www.r-pkg.org/badges/version-last-release/savvyr)
+[![CRAN monthly
+downloads](https://cranlogs.r-pkg.org/badges/savvyr)](https://cranlogs.r-pkg.org/badges/savvyr)
+[![CRAN total
+downloads](https://cranlogs.r-pkg.org/badges/grand-total/savvyr)](https://cranlogs.r-pkg.org/badges/grand-total/savvyr)
+[![Code
+Coverage](https://raw.githubusercontent.com/openpharma/savvyr/_xml_coverage_reports/data/main/badge.svg)](https://raw.githubusercontent.com/openpharma/savvyr/_xml_coverage_reports/data/main/coverage.xml)
+<!-- badges: end -->  
+
+The SAVVY project is a consortium of academic and pharmaceutical
+industry partners that aims to improve the analyses of adverse event
+(AE) data in clinical trials through the use of survival techniques
+appropriately dealing with varying follow-up times and competing events.
+Although statistical methodologies have advanced, in AE analyses often
+the incidence proportion, the incidence density or a non-parametric
+Kaplan-Meier estimator are used, which either ignore censoring or
+competing events. The `savvyr` package contains functions to easily
+conduct the proposed improved AE analyses.
+
+## Installation
+
+### Release
+
+You can install the current release version from *CRAN* with:
+
+``` r
+install.packages("savvyr")
+```
+
+### Development
+
+You can install the development version of savvyr from
+[GitHub](https://github.com/) with:
+
+``` r
+if (!require("remotes")) {
+  install.packages("remotes")
+}
+remotes::install_github("openpharma/savvyr")
+```
+
+## Getting Started
+
+See the [introductory
+vignette](https://openpharma.github.io/savvyr/main/articles/introduction.html)
+to get started.
+
+## Citing `savvyr`
+
+To cite `savvyr` please see
+[here](https://openpharma.github.io/savvyr/main/authors.html#citation).

inst/WORDLIST

@@ -3,33 +3,20 @@ AdVerse
 AE
 al
 Beyersmann
-BiocCheck
-Bove
-CMD
+Bové
+doi
 et
-Forkers
 funder
-github
-gitleaks
 Hoffmann
-initializer
 Johansen
 Kaplan
 Kaspar
 Kuenzel
-L<U+00FC>bbert
-Pkgdown
-Pre
-pre
-repo
-Roxygen
 Rufibach
-Sabanes
+Sabanés
 savvyr
 Schmoor
 Stegherr
-SuperLinter
 Ulm
-Universit
-Universit<U+00E4>t
+Universität
 VarYing

vignettes/savvyr_example.Rmd → vignettes/introduction.Rmd

@@ -1,13 +1,13 @@
 ---
-title: "Estimation of AE probabilities with savvyr"
+title: "Introduction to `savvyr`"
 package: savvyr
 bibliography: "../inst/REFERENCES.bib"
 output:
   rmarkdown::html_vignette:
           toc: true
 vignette: |
   %\VignetteEncoding{UTF-8}
-  %\VignetteIndexEntry{Estimation of AE probabilities with savvyr}
+  %\VignetteIndexEntry{Introduction to `savvyr`}
   %\VignetteEngine{knitr::rmarkdown}
 editor_options:
   chunk_output_type: console
@@ -22,16 +22,16 @@ library(savvyr)
 library(kableExtra)
 ```
 
-
-
+In this vignette we give an introduction how to use `savvyr` to estimate adverse event probabilities using the SAVVY framework.
 
 # Example using dummy data
 
 We generate the dataset $S1$ in @stegherr_meta_analytic_2021 using the parameter
-values for Arm A.  
-First we define sample size and range of censoring times. Then we set the hazard
+values for Arm A.
+First we define the sample size and a range of censoring times.
+Then we set the hazard
 of the three event types (adverse event, death/hard competing event and soft
-competing event). After the dataset has been generated we set $\tau$ as the
+competing event). After the dataset has been generated, we set $\tau$ as the
 maximum event time.
 
 ```{r, include=TRUE, echo=TRUE}
@@ -40,12 +40,14 @@ n <- 200
 min_cens <- 0
 max_cens <- 1000
 
-haz_ae <- 0.00265
-haz_death <- 0.00151
-haz_soft <- 0.00227
-
 set.seed(2020)
-dat1 <- generate_data(n, cens = c(min_cens, max_cens), haz_ae, haz_death, haz_soft)
+dat1 <- generate_data(
+  n,
+  cens = c(min_cens, max_cens),
+  haz_ae = 0.00265,
+  haz_death = 0.00151,
+  haz_soft = 0.00227
+)
 
 tau <- max(dat1[, "time_to_event"])
 ```
@@ -56,10 +58,10 @@ The structure of the dataset looks as follows:
 kable(head(dat1, 10), align = c("crcr"))
 ```
 
-For this dataset we then compute all the estimators used in the comparisons 
-in @stegherr_survival_2021 and @stegherr_estimating_2021.  
+For this dataset we then compute all the estimators used in the comparisons
+in @stegherr_survival_2021 and @stegherr_estimating_2021.
 We start with the estimators that do not account for competing events (incidence
-proportion, incidence density, Inverse Kaplan Meier), then incidence proportion 
+proportion, incidence density, Inverse Kaplan Meier), then incidence proportion
 accounting for competing events and Aalen-Johansen (both first with death only
 as hard competing event, then using all competing events):