Residual balancing is a robust method of constructing weights for marginal structural models, which can be used to estimate (a) the average treatment effect in a cross-sectional observational study, (b) controlled direct/mediator effects in causal mediation analysis, and (c) the effects of time-varying treatments in panel data (Zhou and Wodtke 2020 <doi:10.1017/pan.2020.2>). This package provides three functions, rbwPoint(), rbwMed(), and rbwPanel(), that produce residual balancing weights for estimating (a), (b), (c), respectively.

Display spatial data with interactive maps powered by the open- source JavaScript library Leaflet (see <https://leafletjs.com/>). Maps can be rendered in a web browser or displayed in the HTML viewer pane of RStudio'. This package is designed to be easy to use and can create complex maps with vector and raster data, web served map tiles and interface elements.

Enhances the R Optimization Infrastructure ('ROI') package with the quadratic solver OSQP'. More information about OSQP can be found at <https://osqp.org>.

Reads data files acquired by Bruker Daltonics matrix-assisted laser desorption/ionization-time-of-flight mass spectrometer of the *flex series.

This package implements methods described by the paper Robins and Tsiatis (1991) <DOI:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.

Easy installation, loading, and control of packages for redistricting data downloading, spatial data processing, simulation, analysis, and visualization. This package makes it easy to install and load multiple redistverse packages at once. The redistverse is developed and maintained by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. For more details see <https://alarm-redist.org>.

This package provides an R6 class and several utility methods to facilitate the implementation of models based on ordinary differential equations. The heart of the package is a code generator that creates compiled Fortran (or R') code which can be passed to a numerical solver. There is direct support for solvers contained in packages deSolve and rootSolve'.

Presentation-ready results tables for epidemiologists in an automated, reproducible fashion. The user provides the final analytical dataset and specifies the design of the table, with rows and/or columns defined by exposure(s), effect modifier(s), and estimands as desired, allowing to show descriptors and inferential estimates in one table -- bridging the rift between epidemiologists and their data, one table at a time. See Rothman (2017) <doi:10.1007/s10654-017-0314-3>.

This package implements solutions to canonical models of Economics such as Monopoly Profit Maximization, Cournot's Duopoly, Solow (1956, <doi:10.2307/1884513>) growth model and Mankiw, Romer and Weil (1992, <doi:10.2307/2118477>) growth model.

Read raw and processed data from acoustic ejection mass spectrometry (AEMS) files produced by the Sciex EchoMS instrument. Includes functions to create interactive reader objects, extract raw intensity measurements, mass spectra, and fully-processed mass-transition intensity areas. Methods for data processing and analysis are described in Rimmer et al. (2025) <doi:10.1021/acs.analchem.5c03730>. Supports both multiple reaction monitoring (MRM) and full-scan (neutral loss and precursor ion) data formats.

Import Data from Relational Database Management Systems (RDBMS) and Health Information Systems ('HIS'). The current version of the package supports importing data from RDBMS including MS SQL', MySQL', PostGRESQL', and SQLite', as well as from two HIS platforms: DHIS2 and SORMAS'.

Calculate common survey data quality indicators for multi-item scales and matrix questions. Currently supports the calculation of response style indicators and response distribution indicators. For an overview on response quality indicators see Bhaktha N, Henning S, Clemens L (2024). Characterizing response quality in surveys with multi-item scales: A unified framework <https://osf.io/9gs67/>.

Extends the functionality of the pROC package for conducting smoothed receiver operating characteristic (ROC) curve analysis. Enables automated selection of the distribution families to be fit when smoothing ROC curves via the population probability density function estimation strategy described by Leeflang et al. (2008) <doi:10.1373/clinchem.2007.096032>, as well as generation of diagnostic performance and cutoff estimates from the resultant smoothed curves.

Flexible rounding functions for use in error detection. They were outsourced from the scrutiny package.

An implementation of the radviz projection in R. It enables the visualization of multidimensional data while maintaining the relation to the original dimensions. This package provides functions to create and plot radviz projections, and a number of summary plots that enable comparison and analysis. For reference see Hoffman *et al.* (1999) (<doi:10.1145/331770.331775>) for original implementation, see Di Caro *et al* (2012) (<doi:10.1007/978-3-642-13672-6_13>), for the original method for dimensional anchor arrangements, see Demsar *et al.* (2007) (<doi:10.1016/j.jbi.2007.03.010>) for the original Freeviz implementation.

An RStudio addin providing a visual, point-and-click interface for data manipulation and analysis, designed for users transitioning from SPSS to R. Every visual action generates clean, reproducible R code using tidyverse conventions ('dplyr', tidyr'). Includes a context-aware AI copilot supporting multiple LLM providers ('OpenAI', Anthropic Claude, Google Gemini') with built-in proxy support and privacy controls.

Code to facilitate simulation and inference when connectivity is defined by underlying random walks. Methods for spatially-correlated pairwise distance data are especially considered. This provides core code to conduct analyses similar to that in Hanks and Hooten (2013) <doi:10.1080/01621459.2012.724647>.

Three methods to calculate R2 for models with correlated errors, including Phylogenetic GLS, Phylogenetic Logistic Regression, Linear Mixed Models (LMMs), and Generalized Linear Mixed Models (GLMMs). See details in Ives 2018 <doi:10.1093/sysbio/syy060>.

Computation of the International Roughness Index (IRI) given a longitudinal road profile. The IRI can be calculated for a single road segment or for a sequence of segments with a fixed length (e. g. 100m). For the latter, an overlap of the segments can be selected. The IRI and likewise the algorithms for its determination are defined in Sayers, Michael W; Gillespie, Thomas D; Queiroz, Cesar A.V. 1986. The International Road Roughness Experiment (IRRE) : establishing correlation and a calibration standard for measurements. World Bank technical paper; no. WTP 45. Washington, DC : The World Bank. (ISBN 0-8213-0589-1) available from <http://documents.worldbank.org/curated/en/326081468740204115>.

The RMM fits Revenue Management Models using the RDE(Robust Demand Estimation) method introduced in the paper by <doi:10.2139/ssrn.3598259>, one of the customer choice-based Revenue Management Model. Furthermore, it is possible to select a multinomial model as well as a conditional logit model as a model of RDE.

This package provides clean, tidy access to statistical data published by the European Central Bank ('ECB') via the ECB Data Portal API <https://data.ecb.europa.eu>. Covers policy interest rates, EURIBOR', euro exchange rates, harmonised consumer price inflation ('HICP'), euro area yield curves, the euro short-term rate ('ESTR'), monetary aggregates (M1, M2, M3), mortgage and lending rates, GDP, unemployment, and government debt-to-GDP. Each dataset has a dedicated function that abstracts away the underlying SDMX key structure, so users do not need to know series codes. A generic fetcher is also provided for direct access to any of the ECB 100-plus dataflows. Data is downloaded on first use and cached locally for subsequent calls.

Interface to SWI'-'Prolog', <https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package rolog instead. The purpose of this package is to provide the Prolog runtime on systems that do not have a software installation of SWI'-'Prolog'.

This package provides tools for getting historical weather information and forecasts from wunderground.com. Historical weather and forecast data includes, but is not limited to, temperature, humidity, windchill, wind speed, dew point, heat index. Additionally, the weather underground weather API also includes information on sunrise/sunset, tidal conditions, satellite/webcam imagery, weather alerts, hurricane alerts and historical high/low temperatures.

Traditional noise filtering methods aim at removing noisy samples from a classification dataset. This package adapts classic and recent filtering techniques for use in regression problems, and it also incorporates methods specifically designed for regression data. In order to do this, it uses approaches proposed in the specialized literature, such as Martin et al. (2021) [<doi:10.1109/ACCESS.2021.3123151>] and Arnaiz-Gonzalez et al. (2016) [<doi:10.1016/j.eswa.2015.12.046>]. Thus, the goal of the implemented noise filters is to eliminate samples with noise in regression datasets.