This package provides a set of segregation-based indices and randomization methods to make robust environmental inequality assessments, as described in Schaeffer and Tivadar (2019) "Measuring Environmental Inequalities: Insights from the Residential Segregation Literature" <doi:10.1016/j.ecolecon.2019.05.009>.

Enables reading and writing binary and ASCII data to RS232/RS422/RS485 or any other virtual serial interface of the computer.

Sejong(http://www.sejong.or.kr/) corpus and Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum) dictionaries for KoNLP.

Translates SAS programs and projects into R code, orders connected programs by their dependencies, and executes generated code. Compares generated datasets with supplied reference outputs and reports unresolved translation and validation findings. Rule-based translation works offline without a SAS installation. Optional language model translation, review, and repair use a user-configured provider through ellmer and may require an account and API credentials.

Estimation, scoring, and plotting functions for the semi-parametric factor model proposed by Liu & Wang (2022) <doi:10.1007/s11336-021-09832-8> and Liu & Wang (2023) <arXiv:2303.10079>. Both the conditional densities of observed responses given the latent factors and the joint density of latent factors are estimated non-parametrically. Functional parameters are approximated by smoothing splines, whose coefficients are estimated by penalized maximum likelihood using an expectation-maximization (EM) algorithm. E- and M-steps can be parallelized on multi-thread computing platforms that support OpenMP'. Both continuous and unordered categorical response variables are supported.

This package provides a graphical user interface for cross-sectional network modeling with the statnet software suite <https://github.com/statnet>.

Simulates regression models, including both simple regression and generalized linear mixed models with up to three level of nesting. Power simulations that are flexible allowing the specification of missing data, unbalanced designs, and different random error distributions are built into the package.

Calibration of thresholds of control charts such as CUSUM charts based on past data, taking estimation error into account.

This package provides a flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples.

This package implements Support Vector Machine Oblique Decision Trees (SVMODT). Recursively builds classification trees using linear Support Vector Machines (SVM) hyperplanes at each node instead of axis-parallel splits, creating oblique decision boundaries. Features include multiple feature selection methods, dynamic feature subset strategies, class weight support for imbalanced datasets, pruning and feature penalization.

This package provides data and functions used in the book "Statistical Methods for Quantitative Finance" by David Ardia (2026).

Makes the React library Chakra UI usable in Shiny apps. Chakra UI components include alert dialogs, drawers (sliding panels), menus, modals, popovers, sliders, and more.

Generates synthetic ADaM (Analysis Data Model) datasets from real clinical trial data. Preserves the structure of real datasets (column names, value ranges, relationships between treatment and flag columns) while removing identifiable patient information. Supports subject-level (ADSL), longitudinal (BDS), occurrence (OCCDS), and time-to-event (TTE) dataset types.

This package provides an S4 class for representing and interacting with sparse plus rank matrices. At the moment the implementation is quite spare, but the plan is eventually subclass Matrix objects.

This package provides tools for analyzing and understanding the file contents of large shiny application directories. The package extracts key information about render functions, reactive functions, and their inputs from app files, organizing them into structured data frames for easy reference. This streamlines the onboarding process for new contributors and helps identify areas for optimization in complex shiny codebases with multiple files and sourcing chains.

Mosaic diagram, scatterplot matrix, Andrews curves, parallel coordinate diagram, radar diagram, and Chernoff plots as a Shiny app, which allow the order of variables to be changed interactively. The apps are intended as teaching examples.

This package provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) <arXiv:1705.07874> The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package shapper is a port of the Python library shap'.

Computes sparse vector autoregressive coefficients and sparse precision matrices for time series chain graphical models. Methods are described in Abegaz and Wit (2013) <doi:10.1093/biostatistics/kxt005>.

Proposes an original instrument for measuring stakeholder influence on the development of an infrastructure project that is carried through by a municipality, drawing on stakeholder classifications (Mitchell, Agle, & Wood, 1997) and input-output modelling (Hester & Adams, 2013). Mitchell R., Agle B.R., & Wood D.J. <doi:10.2307/259247> Hester, P.T., & Adams, K.M. (2013) <doi:10.1016/j.procs.2013.09.282>.

SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models.

An advanced version of package s2dverification'. Intended for seasonal to decadal (s2d) climate forecast verification, but also applicable to other types of forecasts or general climate analysis. This package is specifically designed for comparing experimental and observational datasets. It provides functionality for data retrieval, post-processing, skill score computation against observations, and visualization. Compared to s2dverification', s2dv is more compatible with the package startR', able to use multiple cores for computation and handle multi-dimensional arrays with a higher flexibility. The Climate Data Operators (CDO) version used in development is 1.9.8. Implements methods described in Wilks (2011) <doi:10.1016/B978-0-12-385022-5.00008-7>, DelSole and Tippett (2016) <doi:10.1175/MWR-D-15-0218.1>, Kharin et al. (2012) <doi:10.1029/2012GL052647>, Doblas-Reyes et al. (2003) <doi:10.1007/s00382-003-0350-4>.

Vignettes for the survival package. Split from the survival package since the vignettes were getting large. Also, since survival is a recommended package it cannot make use of other packages outside of base+recommended (e.g. rmarkdown').

This package provides a unifying framework for managing and deploying shiny applications that consist of modules, where an "app" is a tab-based workflow that guides a user step-by-step through an analysis. The shinymgr app builder "stitches" shiny modules together so that outputs from one module serve as inputs to the next, creating an analysis pipeline that is easy to implement and maintain. Users of shinymgr apps can save analyses as an RDS file that fully reproduces the analytic steps and can be ingested into an R Markdown report for rapid reporting. In short, developers use the shinymgr framework to write modules and seamlessly combine them into shiny apps, and users of these apps can execute reproducible analyses that can be incorporated into reports for rapid dissemination.

This package provides indices and tools for directed acyclic graphs (DAGs), particularly DAG representations of intermittent streams. A detailed introduction to the package can be found in the publication: "Non-perennial stream networks as directed acyclic graphs: The R-package streamDAG" (Aho et al., 2023) <doi:10.1016/j.envsoft.2023.105775>, and in the introductory package vignette.