Data cleaning including 1) generating datasets for time-series and case-crossover analyses based on raw hospital records, 2) linking individuals to an areal map, 3) picking out cases living within a buffer of certain size surrounding a site, etc. For more information, please refer to Zhang W,etc. (2018) <doi:10.1016/j.envpol.2018.08.030>.

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The rdpower package provides tools to perform power, sample size and MDE calculations in RD designs: rdpower() calculates the power of an RD design, rdsampsi() calculates the required sample size to achieve a desired power and rdmde() calculates minimum detectable effects. See Cattaneo, Titiunik and Vazquez-Bare (2019) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2019_Stata.pdf> for further methodological details.

Reproducible, programmatic retrieval of datasets from the Roper Center data archive. The Roper Center for Public Opinion Research <https://ropercenter.cornell.edu> maintains the largest archive of public opinion data in existence, but researchers using these datasets are caught in a bind. The Center's terms and conditions bar redistribution of downloaded datasets, but to ensure that one's work can be reproduced, assessed, and built upon by others, one must provide access to the raw data one employed. The `ropercenter` package cuts this knot by providing registered users with programmatic, reproducible access to Roper Center datasets from within R.

Connect R with MOA (Massive Online Analysis - <https://moa.cms.waikato.ac.nz/>) to build classification models and regression models on streaming data or out-of-RAM data. Also streaming recommendation models are made available.

An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data as presented in Sarrias (2016) <doi:10.18637/jss.v074.i10>.

Implementation of a Recurrent Neural Network architectures in native R, including Long Short-Term Memory (Hochreiter and Schmidhuber, <doi:10.1162/neco.1997.9.8.1735>), Gated Recurrent Unit (Chung et al.) and vanilla RNN.

Selected functions for simulation and regression of integrated Isothermal Titration Calorimetry (ITC) data with the most commonly used one-to-one binding model.

Supports modelling real-time case data to facilitate the real-time surveillance of infectious diseases and other point phenomena. The package provides automated computational grid generation over an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, and predictions and visualisation. Both Bayesian and maximum likelihood methods are provided. Log-Gaussian Cox Processes are described by Diggle et al. (2013) <doi:10.1214/13-STS441> and we provide both the low-rank approximation for Gaussian processes described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and Riutort-Mayol et al (2023) <doi:10.1007/s11222-022-10167-2> and the nearest neighbour Gaussian process described by Datta et al (2016) <doi:10.1080/01621459.2015.1044091>.

Combined with RRphylo', this package provides a powerful tool to analyse and visualise 3d models (surfaces and meshes) in a phylogenetically explicit context (Melchionna et al., 2024 <doi:10.1038/s42003-024-06710-8>).

Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions. Includes transport routines in porous media, in estuaries, and in bodies with variable shape.

Read Acoustic HAC format.

This package provides functions for radiation safety, also known as "radiation protection" and "radiological control". The science of radiation protection is called "health physics" and its engineering functions are called "radiological engineering". Functions in this package cover many of the computations needed by radiation safety professionals. Examples include: obtaining updated calibration and source check values for radiation monitors to account for radioactive decay in a reference source, simulating instrument readings to better understand measurement uncertainty, correcting instrument readings for geometry and ambient atmospheric conditions. Many of these functions are described in Johnson and Kirby (2011, ISBN-13: 978-1609134198). Utilities are also included for developing inputs and processing outputs with radiation transport codes, such as MCNP, a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron, or coupled neutron/photon/electron transport (Werner et. al. (2018) <doi:10.2172/1419730>).

Iterative least cost path and minimum spanning tree methods for projecting forest road networks. The methods connect a set of target points to an existing road network using igraph <https://igraph.org> to identify least cost routes. The cost of constructing a road segment between adjacent pixels is determined by a user supplied weight raster and a weight function; options include the average of adjacent weight raster values, and a function of the elevation differences between adjacent cells that penalizes steep grades. These road network projection methods are intended for integration into R workflows and modelling frameworks used for forecasting forest change, and can be applied over multiple time-steps without rebuilding a graph at each time-step.

Implementation of the Johnson Quantile-Parameterised Distribution in R. The Johnson Quantile-Parameterised Distribution (J-QPD) is a flexible distribution system that is parameterised by a symmetric percentile triplet of quantile values (typically the 10th-50th-90th) along with known support bounds for the distribution. The J-QPD system was developed by Hadlock and Bickel (2017) <doi:10.1287/deca.2016.0343>. This package implements the density, quantile, CDF and random number generator functions.

Build regular expressions piece by piece using human readable code. This package contains date and time functionality, and is primarily intended to be used by package developers.

This package provides formatting linting to roxygen2 tags. Linters report roxygen2 tags that do not conform to a standard style. These linters can be a helpful check for building more consistent documentation and to provide reminders about best practices or checks for typos. Default linting suites are provided for common style guides such as the one followed by the tidyverse', though custom linters can be registered by other packages or be custom-tailored to a specific package.

The goal of rlowdb is to provide a lightweight, file-based JSON database. Inspired by LowDB in JavaScript', it generates an intuitive interface for storing, retrieving, updating, and querying structured data without requiring a full-fledged database system. Ideal for prototyping, small-scale applications, and lightweight data management needs.

The rkafkajars package collects all the external jars required for the rkafka package.

This package contains example data for the rehh package.

Perform robust estimation and inference in platform trials and other master protocol trials. Yuhan Qian, Yifan Yi, Jun Shao, Yanyao Yi, Gregory Levin, Nicole Mayer-Hamblett, Patrick J. Heagerty, Ting Ye (2025) <doi:10.48550/arXiv.2411.12944>.

This package provides tools to help developers and producers manipulate R objects and outputs. It includes tools for displaying results and objects, and for formatting them in the correct format.

This package provides streamlined functions for summarising and visualising regression models fitted with the rms package, in the preferred format for medical journals. The modelsummary_rms() function produces concise summaries for linear, logistic, and Cox regression models, including automatic handling of models containing restricted cubic spline (RCS) terms. The resulting summary dataframe can be easily converted into publication-ready documents using the flextable and officer packages. The ggrmsMD() function creates clear and customizable plots ('ggplot2 objects) to visualise RCS terms.

An interface to the powerful and fairly complete computer algebra system Maxima'. It can be used to start and control Maxima from within R by entering Maxima commands. Results from Maxima can be parsed and evaluated in R. It facilitates outputting results from Maxima in LaTeX and MathML'. 2D and 3D plots can be displayed directly. This package also registers a knitr'-engine enabling Maxima code chunks to be written in RMarkdown documents.

This package provides functions to perform propensity score matching on rolling entry interventions for which a suitable "entry" date is not observed for nonparticipants. For more details, please reference Witman et al. (2018) <doi:10.1111/1475-6773.13086>.