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R documentation patterns with roxygen2, pkgdown, vignettes, examples, and package site structure.
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Basé sur la classification professionnelle SOC
Bayesian meta-analysis models including fixed effects, random effects, and network meta-analysis with Stan and JAGS implementations.
Foundational knowledge for writing current PyMC models including syntax, distributions, sampling, and ArviZ diagnostics. Use when creating or reviewing PyMC models.
Foundational knowledge for writing modern Stan models including program structure, type system, distributions, and best practices. Use when creating or reviewing Stan models.
Group sequential design methods for interim analyses, alpha spending, and futility stopping. Use when designing trials with interim looks or implementing spending functions.
Core Mediana package functions for Clinical Scenario Evaluation (CSE). Use when designing data models, analysis models, evaluation models, and running comprehensive trial simulations.
Core simtrial package functions for time-to-event clinical trial simulation. Use when generating survival data, performing weighted logrank tests, or running TTE simulations.
| name | r-documentation-patterns |
| description | R documentation patterns with roxygen2, pkgdown, vignettes, examples, and package site structure. |
Best practices and patterns for documenting R code, packages, and projects. Covers README files, code comments, function documentation, and project-level documentation.
# Package Name
<!-- badges: start -->
[](https://github.com/user/pkg/actions/workflows/R-CMD-check.yaml)
[](https://CRAN.R-project.org/package=pkg)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://app.codecov.io/gh/user/pkg?branch=main)
<!-- badges: end -->
## Overview
Brief description of what the package does (2-3 sentences).
## Installation
```r
# From CRAN
install.packages("pkg")
# Development version from GitHub
# install.packages("pak")
pak::pak("user/pkg")
library(pkg)
# Basic example
result <- main_function(data)
MIT © Author Name
### Badges
```r
# Common badges
usethis::use_badge("CRAN status")
usethis::use_lifecycle_badge("stable")
usethis::use_github_actions_badge("R-CMD-check")
usethis::use_coverage("codecov")
# Good: Explain WHY, not WHAT
# Normalize by library size to account for sequencing depth differences
normalized_counts <- counts / library_sizes * 1e6
# Bad: Restates the code
# Divide counts by library sizes and multiply by 1e6
normalized_counts <- counts / library_sizes * 1e6
# Data Loading ----
# Load and validate input data
data <- read_csv("input.csv")
validate_data(data)
# Preprocessing ----
# Clean and transform variables
clean_data <- data |>
remove_missing() |>
transform_variables()
# Analysis ----
# Fit statistical model
model <- fit_model(clean_data)
# TODO: Add support for multiple response variables
# FIXME: Handle edge case when n < 10
# NOTE: This assumes data is sorted by date
# HACK: Temporary workaround until upstream fix
#' Calculate Body Mass Index
#'
#' Computes BMI from weight in kilograms and height in meters.
#'
#' @param weight Numeric vector of weights in kilograms.
#' @param height Numeric vector of heights in meters.
#'
#' @return Numeric vector of BMI values.
#'
#' @examples
#' calculate_bmi(70, 1.75)
#' calculate_bmi(c(70, 80), c(1.75, 1.80))
#'
#' @export
calculate_bmi <- function(weight, height) {
weight / height^2
}
#' Fit Survival Model with Cross-Validation
#'
#' Fits a Cox proportional hazards model using k-fold cross-validation
#' to assess predictive performance.
#'
#' @param formula A formula specifying the model. The response should be
#' created using [survival::Surv()].
#' @param data A data frame containing the variables in the formula.
#' @param folds Integer. Number of cross-validation folds. Default is 10.
#' @param stratify Logical. Whether to stratify folds by event status.
#' Default is `TRUE`.
#' @param seed Integer. Random seed for reproducibility. Default is `NULL`.
#'
#' @return A list with class `cv_surv` containing:
#' \describe{
#' \item{model}{The final model fit on all data.}
#' \item{cv_performance}{Data frame of cross-validation metrics.}
#' \item{fold_predictions}{Predictions for each fold.}
#' }
#'
#' @details
#' The function uses [rsample::vfold_cv()] for creating folds and
#' calculates concordance index (C-statistic) for each fold.
#'
#' @section Model Assumptions:
#' The Cox model assumes proportional hazards. Check this assumption
#' using [survival::cox.zph()] on the returned model.
#'
#' @seealso
#' * [survival::coxph()] for fitting Cox models
#' * [survival::Surv()] for creating survival objects
#' * [rsample::vfold_cv()] for cross-validation
#'
#' @examples
#' library(survival)
#'
#' # Fit model with cross-validation
#' result <- cv_survival(
#' Surv(time, status) ~ age + sex,
#' data = lung,
#' folds = 5
#' )
#'
#' # View performance
#' result$cv_performance
#'
#' @export
cv_survival <- function(formula, data, folds = 10, stratify = TRUE, seed = NULL) {
# Implementation
}
Package: pkgname
Title: What the Package Does (One Line, Title Case)
Version: 0.1.0
Authors@R:
person("First", "Last", , "email@example.com", role = c("aut", "cre"),
comment = c(ORCID = "YOUR-ORCID-ID"))
Description: What the package does (one paragraph).
More details about the package can go here across
multiple lines.
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
URL: https://github.com/user/pkgname
BugReports: https://github.com/user/pkgname/issues
Depends:
R (>= 4.1)
Imports:
dplyr (>= 1.0.0),
ggplot2
Suggests:
testthat (>= 3.0.0),
knitr,
rmarkdown
VignetteBuilder: knitr
Config/testthat/edition: 3
#' pkgname: A package for doing something useful
#'
#' The pkgname package provides tools for X, Y, and Z.
#'
#' @section Main functions:
#' * [main_function()] - Does the main thing
#' * [helper_function()] - Helps with stuff
#'
#' @section Getting started:
#' See `vignette("pkgname")` for an introduction.
#'
#' @docType package
#' @name pkgname-package
#' @aliases pkgname
"_PACKAGE"
---
title: "Getting Started with pkgname"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Getting Started with pkgname}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(pkgname)
Brief introduction to the package and its purpose.
install.packages("pkgname")
Walk through a simple example.
# Example code
result <- main_function(example_data)
Cover more complex use cases.
vignette("advanced-topics") for more details
## Architecture Documentation
### Mermaid Diagrams
```markdown
## Package Architecture
```mermaid
flowchart TD
A[User Input] --> B[Input Validation]
B --> C{Valid?}
C -->|Yes| D[Data Processing]
C -->|No| E[Error Message]
D --> F[Model Fitting]
F --> G[Results]
## Component Overview
### Data Layer
- `read_data()` - Import various data formats
- `validate_data()` - Check data integrity
- `transform_data()` - Prepare for analysis
### Analysis Layer
- `fit_model()` - Core model fitting
- `cross_validate()` - Performance assessment
- `tune_params()` - Hyperparameter optimization
### Output Layer
- `summarize_results()` - Generate summaries
- `export_results()` - Save to files
- `visualize_results()` - Create plots
#' Example Patient Data
#'
#' A simulated dataset containing patient information for
#' demonstrating survival analysis methods.
#'
#' @format A data frame with 100 rows and 5 columns:
#' \describe{
#' \item{patient_id}{Character. Unique patient identifier.}
#' \item{age}{Numeric. Patient age in years.}
#' \item{sex}{Factor with levels `"male"` and `"female"`.}
#' \item{time}{Numeric. Follow-up time in days.}
#' \item{status}{Integer. Event indicator (1 = event, 0 = censored).}
#' }
#'
#' @source Simulated data for illustration purposes.
#'
#' @examples
#' data(patients)
#' summary(patients)
"patients"
# Generate documentation
devtools::document()
# Check documentation
devtools::check()
# Preview documentation
?function_name
# Build pkgdown site
pkgdown::build_site()