Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.

Use of Knock Out and Round Robin Techniques in preparing tournament fixtures as discussed in the Book Health and Physical Education by Dr. V K Sharma'(2018,ISBN:978-93-5272-134-4).

This package provides a S3 resource is provided by Amazon Web Services S3 or a S3-compatible object store (such as Minio). The resource can be a tidy file to be downloaded from the object store, or a data lake (such as Delta Lake) Parquet file to be read by Apache Spark.

This package provides a statistical learning method to simultaneously predict a range of target phenotypes using codified and natural language processing (NLP)-derived Electronic Health Record (EHR) data. See Ahuja et al (2020) JAMIA <doi:10.1093/jamia/ocaa079> for details.

This package provides deep learning models for right-censored survival data using the torch backend. Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>. The package is described in El Badisy (2026) <doi:10.32614/RJ-2026-008>.

Package including additional modules for interactive ShinyItemAnalysis application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters.

This package provides a streamlined and user-friendly framework for simulating data in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. This package was designed to generate data for the simulations performed in Pesigan, Russell, and Chow (2025) <doi:10.1037/met0000779>.

Calculates enrolment, graduation, dropout, and programme-switch indicators from the Dutch higher education registration data (1CHO) supplied by DUO. Includes an interactive Shiny dashboard for exploring results.

This package provides a SAS interface, through SASPy'(<https://sassoftware.github.io/saspy/>) and reticulate'(<https://rstudio.github.io/reticulate/>). This package helps you create SAS sessions, execute SAS code in remote SAS servers, retrieve execution results and log, and exchange datasets between SAS and R'. It also helps you to install SASPy and create a configuration file for the connection. Please review the SASPy license file as instructed so that you comply with its separate and independent license.

Ordinary and modified statistics for symmetrical linear regression models with small samples. The supported ordinary statistics include Wald, score, likelihood ratio and gradient. The modified statistics include score, likelihood ratio and gradient. Diagnostic tools associated with the fitted model are implemented. For more details see Medeiros and Ferrari (2017) <DOI:10.1111/stan.12107>.

This package provides tools for making, retrieving, displaying and solving sudoku games. This package is an alternative to the earlier sudoku-solver package, sudoku'. The present package uses a slightly different algorithm, has a simpler coding and presents a few more sugar tools, such as plot and print methods. Solved sudoku games are of some interest in Experimental Design as examples of Latin Square designs with additional balance constraints.

This package provides a simple package facilitating ML based analysis for physics education research (PER) purposes. The implemented machine learning technique is random forest optimized by item response theory (IRT) for feature selection and genetic algorithm (GA) for hyperparameter tuning. The data analyzed here has been made available in the CRAN repository through the spheredata package. The SPHERE stands for Students Performance in Physics Education Research (PER). The students are the eleventh graders learning physics at the high school curriculum. We follow the stream of multidimensional students assessment as probed by some research based assessments in PER. The goal is to predict the students performance at the end of the learning process. Three learning domains are measured including conceptual understanding, scientific ability, and scientific attitude. Furthermore, demographic backgrounds and potential variables predicting students performance on physics are also demonstrated.

This package implements user interfaces for log-linear models, Bayesian model averaging and Bayesian Dirichlet process mixture models. See McIntyre, Fellows, Gutreuter and Hladik (2022) <doi:10.2196/32645>.

This package creates shiny application ('app.R') for making predictions based on lm(), glm(), or coxph() models.

This package provides programmatic access to official environmental statistics and data of Peru from the National Environmental Information System ('SINIA', <https://sinia.minam.gob.pe/>), Ministry of the Environment ('MINAM'). Includes indicator catalogs, technical metadata sheets ('fichas técnicas'), and structured historical series for R'.

Function for the GUI API to interact with external IDE/code editors.

Given a list of substance compositions, a list of substances involved in a process, and a list of constraints in addition to mass conservation of elementary constituents, the package contains functions to build the substance composition matrix, to analyze the uniqueness of process stoichiometry, and to calculate stoichiometric coefficients if process stoichiometry is unique. (See Reichert, P. and Schuwirth, N., A generic framework for deriving process stoichiometry in enviromental models, Environmental Modelling and Software 25, 1241-1251, 2010 for more details.).

Creating a great user interface for your Shiny apps can be a hassle, especially if you want to work purely in R and don't want to use, for instance HTML templates. This package adds support for a powerful UI library Fomantic UI - <https://fomantic-ui.com/> (before Semantic). It also supports universal UI input binding that works with various DOM elements.

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.

This package provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways.

It contains functions to estimate multivariate Student's t dynamic and static regression models for given degrees of freedom and lag length. Users can also specify the trends and dummies of any kind in matrix form. Poudyal, N., and Spanos, A. (2022) <doi:10.3390/econometrics10020017>. Spanos, A. (1994) <http://www.jstor.org/stable/3532870>.

Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints.

This package provides estimation of Bayesian vector autoregression (BVAR) models with steady-state priors via Stan', along with functions for unconditional and conditional forecasting, as well as impulse response analysis. For details on the steady-state BVAR model see Villani (2009) <doi:10.1002/jae.1065>.

Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers.