This package contains all phrasal verbs listed in <https://www.englishclub.com/ref/Phrasal_Verbs/> as data frame. Useful for educational purpose as well as for text mining.

Efficient algorithm for estimating piecewise exponential hazard models for right-censored data, and is useful for reliable power calculation, study design, and event/timeline prediction for study monitoring.

This package provides a set of tools to extract bibliographic content from PubMed database using NCBI REST API <https://www.ncbi.nlm.nih.gov/home/develop/api/>.

This package provides functionality for the prior and posterior projected Polya tree for the analysis of circular data (Nieto-Barajas and Nunez-Antonio (2019) <arXiv:1902.06020>).

Allow to run pylint on Python files with a R command or a RStudio addin. The report appears in the RStudio viewer pane as a formatted HTML file.

sqliter helps users, mainly data munging practioneers, to organize their sql calls in a clean structure. It simplifies the process of extracting and transforming data into useful formats.

Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.

This package provides wrappers for scclust', a C library for computationally efficient size-constrained clustering with near-optimal performance. See <https://github.com/fsavje/scclust> for more information.

Format a number (or a list of numbers) to a string (or a list of strings) with SI prefix. Use SI prefixes as constants like (4 * milli)^2.

Fetch data on targeted public investments from Plataforma +Brasil (SICONV) <http://plataformamaisbrasil.gov.br/>, the responsible system for requests, execution, and monitoring of federal discretionary transfers in Brazil.

Set of tools aimed at wrapping some of the functionalities of the packages tools, utils and codetools into a nicer format so that an IDE can use them.

Accesses raw data via API and calculates social determinants of health measures for user-specified locations in the US, returning them in tidyverse- and sf-compatible data frames.

An extension to the R tidy data environment for automated machine learning. The package allows fitting and cross validation of linear regression and classification algorithms on grouped data.

Tidy standardized mean differences ('SMDs'). tidysmd uses the smd package to calculate standardized mean differences for variables in a data frame, returning the results in a tidy format.

Helper and Wrapper functions for making shiny dashboards more easily. Functions are made modular and lower level functions are exported as well, so many use-cases are supported.

Identifies the optimal confidence level to represent the results of a set of pairwise tests as suggested by Armstrong and Poirier (2025) <doi:10.1017/pan.2024.24>.

Assesses evidence for Zipf's Law of Abbreviation in animal vocalisation using IDs, note class and note duration. The package also provides a web plot function for visualisation.

Feature-based variance-sensitive clustering of omics data. Optimizes cluster assignment by taking into account individual feature variance. Includes several modules for statistical testing, clustering and enrichment analysis.

This package fits generalized linear models efficiently using RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner to help safeguard against convergence issues.

This package lets you easily use Bootstrap icons inside Shiny apps and R Markdown documents. More generally, icons can be inserted in any htmltools document through inline SVG.

This lightweight package that adds progress bar to vectorized R functions apply. The implementation can easily be added to functions where showing the progress is useful e.g. bootstrap.

This package implements the R version of the log4j package. It offers hierarchic loggers, multiple handlers per logger, level based filtering, space handling in messages and custom formatting.

This package provides tools to create and modify network objects. The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.

Alabama stands for Augmented Lagrangian Adaptive Barrier Minimization Algorithm; it is used for optimizing smooth nonlinear objective functions with constraints. Linear or nonlinear equality and inequality constraints are allowed.