This package provides functions for displaying multiple images or scatterplots with a color scale, i.e., heat maps, possibly with projected coordinates. The package relies on the base graphics system, so graphics are rendered rapidly.

Compute an anomaly score for multivariate time series based on the k-nearest neighbors algorithm. Different computations of distances between time series are provided.

This package contains functions to help create an Analysis Results Dataset. The dataset follows industry recommended structure. The dataset can be created in multiple passes, using different data frames as input. Analysis Results Datasets are used in the pharmaceutical and biotech industries to capture analysis in a common tabular data structure.

The ggarrow package is a ggplot2 extension that plots a variety of different arrow segments with many options to customize. The arrowheadr package makes it easy to create custom arrowheads and fins within the parameters that ggarrow functions expect. It has preset arrowheads and a collection of functions to create and transform data for customizing arrows.

This package provides functions to fit the binomial and multinomial additive hazard models and to estimate the contribution of diseases/conditions to the disability prevalence, as proposed by Nusselder and Looman (2004) and extended by Yokota et al (2017).

This package provides tools to perform model selection alongside estimation under Linear, Logistic, Negative binomial, Quantile, and Skew-Normal regression. Under the spike-and-slab method, a probability for each possible model is estimated with the posterior mean, credibility interval, and standard deviation of coefficients and parameters under the most probable model.

Using of the accelerated line search algorithm for simultaneously diagonalize a set of symmetric positive definite matrices.

Offers a set of functions to easily make predictions for univariate time series. autoTS is a wrapper of existing functions of the forecast and prophet packages, harmonising their outputs in tidy dataframes and using default values for each. The core function getBestModel() allows the user to effortlessly benchmark seven algorithms along with a bagged estimator to identify which one performs the best for a given time series.

This package provides a function to calculate the concentration of un-ionized ammonia in the total ammonia in aqueous solution using the pH and temperature values.

This package contains functions carrying out adaptive procedures using mixed scaling approach to establish bioequivalence for in-vitro permeation test (IVPT) data. Currently, the package provides procedures based on parallel replicate design and balanced data, according to the U.S. Food and Drug Administration's "Draft Guidance on Acyclovir" <https://www.accessdata.fda.gov/drugsatfda_docs/psg/Acyclovir_topical%20cream_RLD%2021478_RV12-16.pdf>. Potvin et al. (2008) <doi:10.1002/pst.294> provides the basis for our adaptive design (see Method B). For a comprehensive overview of the method, refer to Lim et al. (2023) <doi:10.1002/pst.2333>. This package reflects the views of the authors and should not be construed to represent the views or policies of the U.S. Food and Drug Administration.

This package provides tools for designing and analyzing Acceptance Sampling plans. Supports both Attributes Sampling (Binomial and Poisson distributions) and Variables Sampling (Normal and Beta distributions), enabling quality control for fractional and compositional data. Uses nonlinear programming for sampling plan optimization, minimizing sample size while controlling producer's and consumer's risks. Operating Characteristic curves are available for plan visualization.

This package provides functions to access data from public RESTful APIs including World Bank API and REST Countries API', retrieving real-time or historical information related to Algeria. The package enables users to query economic indicators and international demographic and geopolitical statistics in a reproducible way. It is designed for researchers, analysts, and developers who require reliable and programmatic access to Algerian data through established APIs. For more information on the APIs, see: World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392> and REST Countries API <https://restcountries.com/>.

This package provides a function that implements the acceptance-rejection method in an optimized manner to generate pseudo-random observations for discrete or continuous random variables. Proposed by von Neumann J. (1951), <https://mcnp.lanl.gov/pdf_files/>, the function is optimized to work in parallel on Unix-based operating systems and performs well on Windows systems. The acceptance-rejection method implemented optimizes the probability of generating observations from the desired random variable, by simply providing the probability function or probability density function, in the discrete and continuous cases, respectively. Implementation is based on references CASELLA, George at al. (2004) <https://www.jstor.org/stable/4356322>, NEAL, Radford M. (2003) <https://www.jstor.org/stable/3448413> and Bishop, Christopher M. (2006, ISBN: 978-0387310732).

This package provides a shiny application to assess statistical assumptions and guide users toward appropriate tests. The app is designed for researchers with minimal statistical training and provides diagnostics, plots, and test recommendations for a wide range of analyses. Many statistical assumptions are implemented using the package rstatix (Kassambara, 2019) <doi:10.32614/CRAN.package.rstatix> and performance (Lüdecke et al., 2021) <doi:10.21105/joss.03139>.

Package that simulates adaptive (multi-arm, multi-stage) clinical trials using adaptive stopping, adaptive arm dropping, and/or adaptive randomisation. Developed as part of the INCEPT (Intensive Care Platform Trial) project (<https://incept.dk/>), primarily supported by a grant from Sygeforsikringen "danmark" (<https://www.sygeforsikring.dk/>).

Evaluates acute lymphoblastic leukemia maintenance therapy practice at patient and cohort level.

This package provides a customisable set of tools for assessing and grading R or R-markdown scripts from students. It allows for checking correctness of code output, runtime statistics and static code analysis. The latter feature is made possible by representing R expressions using a tree structure.

This package provides a collection of functions to construct A-optimal block designs for comparing test treatments with one or more control(s). Mainly A-optimal balanced treatment incomplete block designs, weighted A-optimal balanced treatment incomplete block designs, A-optimal group divisible treatment designs and A-optimal balanced bipartite block designs can be constructed using the package. The designs are constructed using algorithms based on linear integer programming. To the best of our knowledge, these facilities to construct A-optimal block designs for comparing test treatments with one or more controls are not available in the existing R packages. For more details on designs for tests versus control(s) comparisons, please see Hedayat, A. S. and Majumdar, D. (1984) <doi:10.1080/00401706.1984.10487989> A-Optimal Incomplete Block Designs for Control-Test Treatment Comparisons, Technometrics, 26, 363-370 and Mandal, B. N. , Gupta, V. K., Parsad, Rajender. (2017) <doi:10.1080/03610926.2015.1071394> Balanced treatment incomplete block designs through integer programming. Communications in Statistics - Theory and Methods 46(8), 3728-3737.

R and C++ functions to perform exact and approximate optimal transport. All C++ methods can be linked to other R packages via their header files.

Fast processing of ArcGIS FeatureCollection protocol buffers in R. It is designed to work seamlessly with httr2 and integrates with sf'.

This package provides a function to calibrate variant effect scores against evidence strength categories defined by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) guidelines. The method computes likelihood ratios of pathogenicity via kernel density estimation of pathogenic and benign score distributions, and derives score intervals corresponding to ACMG/AMP evidence levels. This enables researchers and clinical geneticists to interpret functional and computational variant scores in a reproducible and standardised manner. For details, see Badonyi and Marsh (2025) <doi:10.1093/bioinformatics/btaf503>.

R Interface to AutoKeras <https://autokeras.com/>. AutoKeras is an open source software library for Automated Machine Learning (AutoML). The ultimate goal of AutoML is to provide easily accessible deep learning tools to domain experts with limited data science or machine learning background. AutoKeras provides functions to automatically search for architecture and hyperparameters of deep learning models.

Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, Oâ Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) "An allelic series rare variant association test for candidate gene discovery" <doi:10.1016/j.ajhg.2023.07.001>.

It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics.