Essentials for PK/PD (pharmacokinetics/pharmacodynamics) such as area under the curve, (geometric) coefficient of variation, and other calculations that are not part of base R. This is not a noncompartmental analysis (NCA) package.

Collects a list of your third party R packages, and scans them with the OSS Index provided by Sonatype', reporting back on any vulnerabilities that are found in the third party packages you use.

Scored responses and responses times from the Canadian subsample of the PISA 2018 assessment, accessible as the "Cognitive items total time/visits data file" by OECD (2020) <https://www.oecd.org/pisa/data/2018database/>.

R has no built-in pointer functionality. The pointr package fills this gap and lets you create pointers to R objects, including subsets of dataframes. This makes your R code more readable and maintainable.

Sometimes it is useful to serve up alternative shiny UIs depending on information passed in the request object, such as the value of a cookie or a query parameter. This packages facilitates such switches.

Fits, spatially predicts, and temporally forecasts space-time data using Gaussian Process (GP): (1) spatially varying coefficient process models and (2) spatio-temporal dynamic linear models. Bakar et al., (2016). Bakar et al., (2015).

It allows running Praat scripts from R and it provides some wrappers for basic plotting. It also adds support for literate markdown tangling. The package is designed to bring reproducible phonetic research into R.

This package provides a wrapper for Blizzard's Starcraft II (a 2010 real-time strategy game) Application Programming Interface (API). All documented API calls are implemented in an easy-to-use and consistent manner.

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. Consolidates and extends time series functionality from packages including dplyr', stats', xts', forecast', slider', padr', recipes', and rsample'.

Processing and analysis of pathomics, omics and other medical datasets. tRigon serves as a toolbox for descriptive and statistical analysis, correlations, plotting and many other methods for exploratory analysis of high-dimensional datasets.

Univariate time series operations that follow an opinionated design. The main principle of transx is to keep the number of observations the same. Operations that reduce this number have to fill the observations gap.

To computed the variability independent of mean (VIM) or variation independent of mean (VIM). The methodology can be found at Peter M Rothwell et al. (2010) <doi:10.1016/S1474-4422(10)70067-3>.

Compute surrogate explanation groves for predictive machine learning models and analyze complexity vs. explanatory power of an explanation according to Szepannek, G. and von Holt, B. (2023) <doi:10.1007/s41237-023-00205-2>.

R package accompanying the book Working with dynamic models for agriculture and environment, by Daniel Wallach (INRA), David Makowski (INRA), James W. Jones (U.of Florida), Francois Brun (ACTA). 3rd edition 2018-09-27.

Data from publicly available databases (GTEx, CCLE, TCGA and ENCODE) that go with CTexploreR in order to re-define a comprehensive and thoroughly curated list of CT genes and their main characteristics.

This package provides a set of annotation maps describing the entire Human Disease Ontology. The annotation data comes from https://github.com/DiseaseOntology/HumanDiseaseOntology/tree/main/src/ontology.

This package identifies differential expression in high-throughput count data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods.

The stageR package allows automated stage-wise analysis of high-throughput gene expression data. The method is published in Genome Biology at https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0.

This package provides functions for analyzing multivariate data. Dependencies of the distribution of the specified variable (response variable) to other variables (explanatory variables) are derived and evaluated by the Akaike Information Criterion (AIC).

This package provides an efficient interface to MPI by utilizing S4 classes and methods with a focus on Single Program/Multiple Data (SPMD) parallel programming style, which is intended for batch parallel execution.

This package provides a pure Rust implementation of the Digital Signature Algorithm as specified in FIPS 186-4 (Digital Signature Standard), providing RFC6979 deterministic signatures as well as support for added entropy.

This is a Library to program x86 (amd64) hardware. It contains x86 specific data structure descriptions, data-tables, as well as convenience function to call assembly instructions typically not exposed in higher level languages.

Mio is a fast, low-level I/O library for Rust focusing on non-blocking APIs and event notification for building I/O apps with as little overhead as possible over the OS abstractions.

Mio is a fast, low-level I/O library for Rust focusing on non-blocking APIs and event notification for building I/O apps with as little overhead as possible over the OS abstractions.