Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape constrained regression spline. Function outputs fits and various types of inference.

Estimate one or two cutpoints of a metric or ordinal-scaled variable in the multivariable context of survival data or time-to-event data. Visualise the cutpoint estimation process using contour plots, index plots, and spline plots. It is also possible to estimate cutpoints based on the assumption of a U-shaped or inverted U-shaped relationship between the predictor and the hazard ratio. Govindarajulu, U., and Tarpey, T. (2022) <doi:10.1080/02664763.2020.1846690>.

Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points. A full description of this package is available in Ross, G.J (2015) - "Parametric and nonparametric sequential change detection in R" available at <https://www.jstatsoft.org/article/view/v066i03>.

This package provides a user friendly function crrcbcv to compute bias-corrected variances for competing risks regression models using proportional subdistribution hazards with small-sample clustered data. Four types of bias correction are included: the MD-type bias correction by Mancl and DeRouen (2001) <doi:10.1111/j.0006-341X.2001.00126.x>, the KC-type bias correction by Kauermann and Carroll (2001) <doi:10.1198/016214501753382309>, the FG-type bias correction by Fay and Graubard (2001) <doi:10.1111/j.0006-341X.2001.01198.x>, and the MBN-type bias correction by Morel, Bokossa, and Neerchal (2003) <doi:10.1002/bimj.200390021>.

Estimates the Concordance Correlation Coefficient to assess agreement. The scenarios considered are non-repeated measures, non-longitudinal repeated measures (replicates) and longitudinal repeated measures. It also includes the estimation of the one-way intraclass correlation coefficient also known as reliability index. The estimation approaches implemented are variance components and U-statistics approaches. Description of methods can be found in Fleiss (1986) <doi:10.1002/9781118032923> and Carrasco et al. (2013) <doi:10.1016/j.cmpb.2012.09.002>.

Routines for the graphical representation of correlation matrices by means of correlograms, MDS maps and biplots obtained by PCA, PFA or WALS (weighted alternating least squares); See Graffelman & De Leeuw (2023) <doi: 10.1080/00031305.2023.2186952>.

This package provides measures of effect sizes for summarized continuous variables as well as diagnostic accuracy statistics for 2x2 table data. Includes functions for Cohen's d, robust effect size, Cohen's q, partial eta-squared, coefficient of variation, odds ratio, likelihood ratios, sensitivity, specificity, positive and negative predictive values, Youden index, number needed to treat, number needed to diagnose, and predictive summary index.

Record and generate a gif of your R sessions plots. When creating a visualization, there is inevitably iteration and refinement that occurs. Automatically save the plots made to a specified directory, previewing them as they would be saved. Then combine all plots generated into a gif to show the plot refinement over time.

Data package for the supplementary data in Prem et al. (2017) <doi:10.1371/journal.pcbi.1005697> and Prem et al. <doi:10.1371/journal.pcbi.1009098>. Provides easy access to contact data for 177 countries, for use in epidemiological, demographic or social sciences research.

This package implements the Cumulative Standardized Binomial Exponentially Weighted Moving Average (CSB-EWMA) control chart for monitoring multiple independent streams with binomial outcomes. Provides exact variance calculations, adaptive control limits, post-hoc identification with multiple testing corrections (Bonferroni, Holm, Benjamini-Hochberg), and visualization tools. The method is described in Muritala et al. (2026) <doi:10.48550/arXiv.2601.09968>.

An interactive document on the topic of classification tree analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://kartikeyab.shinyapps.io/CTShiny/>.

Detects a variety of coordinated actions on social media and outputs the network of coordinated users along with related information.

This package provides a simple way to manage application settings by loading configuration values from .env or .ini files. It supports default values, type casting, and environment variable overrides, enabling a clean separation of configuration from code. Ideal for managing credentials, API keys, and deployment-specific settings.

Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <arXiv:1802.00828>.

This package provides a modern and flexible R client for the SurveyCTO', a mobile and offline data collection platform, providing a modern and consistent interface for programmatic access to server resources. Built on top of the httr2 package, it enables secure and efficient data retrieval and returns analysis-ready data through optional tidying. It includes functions to create, upload, and download server datasets, in addition to fetching form data, files, and submission attachments. Robust authentication and request handling make the package suitable for automated survey monitoring and downstream analysis.

The cito package provides a user-friendly interface for training and interpreting deep neural networks (DNN). cito simplifies the fitting of DNNs by supporting the familiar formula syntax, hyperparameter tuning under cross-validation, and helps to detect and handle convergence problems. DNNs can be trained on CPU, GPU and MacOS GPUs. In addition, cito has many downstream functionalities such as various explainable AI (xAI) metrics (e.g. variable importance, partial dependence plots, accumulated local effect plots, and effect estimates) to interpret trained DNNs. cito optionally provides confidence intervals (and p-values) for all xAI metrics and predictions. At the same time, cito is computationally efficient because it is based on the deep learning framework torch'. The torch package is native to R, so no Python installation or other API is required for this package.

This package provides tools for linear fitting with complex variables. Includes ordinary least-squares (zlm()) and robust M-estimation (rzlm()), and complex methods for oft used generics. Originally adapted from the rlm() functions of MASS and the lm() functions of stats'.

Implementation of the CNAIM standard in R. Contains a series of algorithms which determine the probability of failure, consequences of failure and monetary risk associated with electricity distribution companies assets such as transformers and cables. Results are visualized in an easy-to-understand risk matrix.

This package provides routines for fitting Cox models by likelihood based boosting for single event survival data with right censoring or in the presence of competing risks. The methodology is described in Binder and Schumacher (2008) <doi:10.1186/1471-2105-9-14> and Binder et al. (2009) <doi:10.1093/bioinformatics/btp088>.

The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets. This package provides an easy-to-use solution for calculating composite reliability for different assessment types. It allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations. Overall, our approach enhances the reliability of composite assessments, making it suitable for various education contexts.

Offers a diverse collection of datasets focused on cardiovascular and heart disease research, including heart failure, myocardial infarction, aortic dissection, transplant outcomes, cardiovascular risk factors, drug efficacy, and mortality trends. Designed for researchers, clinicians, epidemiologists, and data scientists, the package features clinical, epidemiological, and simulated datasets covering a wide range of conditions and treatments such as statins, anticoagulants, and beta blockers. It supports analyses related to disease progression, treatment effects, rehospitalization, and public health outcomes across various cardiovascular patient populations.

Fits cumulative link models (CLMs) for ordinal categorical data using CmdStanR'. Supports various link functions including logit, probit, cloglog, loglog, cauchit, and flexible parametric links such as Generalized Extreme Value (GEV), Asymmetric Exponential Power (AEP), and Symmetric Power. Models are pre-compiled using the instantiate package for fast execution without runtime compilation. Methods are described in Agresti (2010, ISBN:978-0-470-08289-8), Wang and Dey (2011) <doi:10.1007/s10651-010-0154-8>, and Naranjo, Perez, and Martin (2015) <doi:10.1007/s11222-014-9449-1>.

This package provides a collection of functions that have been developed to assist experimenter in modeling chemical degradation kinetic data. The selection of the appropriate degradation model and parameter estimation is carried out automatically as far as possible and is driven by a rigorous statistical interpretation of the results. The package integrates already available goodness-of-fit statistics for nonlinear models. In addition it allows data fitting with the nonlinear first-order multi-target (FOMT) model.

Use C++ Standard Template Library containers interactively in R. Includes sets, unordered sets, multisets, unordered multisets, maps, unordered maps, multimaps, unordered multimaps, stacks, queues, priority queues, vectors, deques, forward lists, and lists.