Minimal Examples of Using Rust Code in R

Template R package with minimal setup to use Rust code in R without hacks or frameworks. Includes basic examples of importing cargo dependencies, spawning threads and passing numbers or strings from Rust to R. Cargo crates are automatically 'vendored' in the R source package to support offline installation. The GitHub repository for this package has more details and also explains how to set up CI. This project was first presented at 'Erum2018' to showcase R-Rust integration < https://jeroen.github.io/erum2018/>; for a real world use-case, see the 'gifski' package on 'CRAN'.

Read and Play Digital Music (MIDI)

Bindings to 'libfluidsynth' to parse and synthesize MIDI files. It can read MIDI into a data frame, play it on the local audio device, or convert into an audio file.

Easily Send HTML Email Messages

Compose and send out responsive HTML email messages that render perfectly across a range of email clients and device sizes. Helper functions let the user insert embedded images, web link buttons, and 'ggplot2' plot objects into the message body. Messages can be sent through an 'SMTP' server, through the 'Posit Connect' service, or through the 'Mailgun' API service < https://www.mailgun.com/>.

Parse Argument Options

A binding to the minimist JavaScript library. This module implements the guts of optimist's argument parser without all the fanciful decoration.

Query 'R' Versions, Including 'r-release' and 'r-oldrel'

Query the main 'R' 'SVN' repository to find the versions 'r-release' and 'r-oldrel' refer to, and also all previous 'R' versions and their release dates.

Efficient Plotting of Large-Sized Data

A tool to plot data with a large sample size using 'shiny' and 'plotly'. Relatively small samples are obtained from the original data using a specific algorithm. The samples are updated according to a user-defined x range. Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost (2022) < https://github.com/predict-idlab/plotly-resampler>.

Trends and Indices for Monitoring Data

The TRIM model is widely used for estimating growth and decline of animal populations based on (possibly sparsely available) count data. The current package is a reimplementation of the original TRIM software developed at Statistics Netherlands by Jeroen Pannekoek. See < https://www.cbs.nl/en-gb/society/nature-and-environment/indices-and-trends%2d%2dtrim%2d%2d> for more information about TRIM.

Another Approach to Package Installation

The goal of 'pak' is to make package installation faster and more reliable. In particular, it performs all HTTP operations in parallel, so metadata resolution and package downloads are fast. Metadata and package files are cached on the local disk as well. 'pak' has a dependency solver, so it finds version conflicts before performing the installation. This version of 'pak' supports CRAN, 'Bioconductor' and 'GitHub' packages as well.

Fast Polygon to Raster Conversion

Provides a drop-in replacement for rasterize() from the 'raster' package that takes polygon vector or data frame objects, and is much faster. There is support for the main options provided by the rasterize() function, including setting the field used and background value, and options for aggregating multi-layer rasters. Uses the scan line algorithm attributed to Wylie et al. (1967) .

Perform Power Analysis for the RI-CLPM and STARTS Model

Perform user-friendly power analyses for the random intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait autoregressive trait state (STARTS) model. The strategy as proposed by Mulder (2023) is implemented. Extensions include the use of parameter constraints over time, bounded estimation, generation of data with skewness and kurtosis, and the option to setup the power analysis for Mplus.