Cases are matched to controls in an efficient, optimal and computationally flexible way. It uses the idea of sub-sampling in the level of the case, by creating pseudo-observations of controls. The user can select between replacement and without replacement, the number of controls, and several covariates to match upon. See Mamouris (2021) <doi:10.1186/s12874-021-01256-3> for an overview.

Helping biologists to choose the most suitable approach to link their research to conservation. After answering few questions on the data available, geographic and taxonomic scope, conserveR ranks existing methods for conservation prioritization and systematic conservation planning by suitability. The methods data base of conserveR contains 133 methods for conservation prioritization based on a systematic review of > 12,000 scientific publications from the fields of spatial conservation prioritization, systematic conservation planning, biogeography and ecology.

The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers a family of parameter sets that are optimal with regard to a multi-objective target (Monteil et al. <doi:10.5194/hess-24-3189-2020>).

Calculate the distribution of costs for the installation of an elevator based on the different distribution rules.

This package provides functions for hit gene identification and quantification of sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis. Details are in Jeong et al. (2019) <doi:10.1101/gr.245571.118> and Baggerly et al. (2003) <doi:10.1093/bioinformatics/btg173>.

Count transformation models featuring parameters interpretable as discrete hazard ratios, odds ratios, reverse-time discrete hazard ratios, or transformed expectations. An appropriate data transformation for a count outcome and regression coefficients are simultaneously estimated by maximising the exact discrete log-likelihood using the computational framework provided in package mlt', technical details are given in Siegfried & Hothorn (2020) <DOI:10.1111/2041-210X.13383>. The package also contains an experimental implementation of multivariate count transformation models with an application to multi-species distribution models <DOI:10.48550/arXiv.2201.13095>.

This package implements Monte Carlo conditional inference for the parameters of a linear nonnormal regression model.

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 in nested design as is often the case for Workplace-Based Assessments. This package is designed to estimate composite reliability in nested designs using multivariate generalizability theory and enhance the analysis of assessment data. The package allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations, and options to find the set of weights that maximizes the composite reliability or minimizes the standard error of measurement (SEM).

Easily cache and retrieve computation results. The package works seamlessly across interactive R sessions, R scripts and Rmarkdown documents.

Recalibrate risk scores (predicting binary outcomes) to improve clinical utility of risk score using weighted logistic or constrained logistic recalibration methods. Additionally, produces plots to assess the potential for recalibration to improve the clinical utility of a risk model. Methods are described in detail in Mishra, A. (2019) "Methods for Risk Markers that Incorporate Clinical Utility" <http://hdl.handle.net/1773/44068>.

One way to choose the number of principal components is via the reconstruction error. This package is designed mainly for this purpose. Graphical representation is also supported, plus some other principal component analysis related functions. References include: Jolliffe I.T. (2002). Principal Component Analysis. <doi:10.1007/b98835> and Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press.

Spatial regression models with compositional responses using the alpha--transformation. Relevant papers include: Tsagris M. (2025), <doi:10.48550/arXiv.2510.12663>, Tsagris M. (2015), <https://soche.cl/chjs/volumes/06/02/Tsagris(2015).pdf>, Tsagris M.T., Preston S. and Wood A.T.A. (2011), <doi:10.48550/arXiv.1106.1451>.

Shiny Web Application for the Multichannel Attribution Problem. It is a user-friendly graphical interface for package ChannelAttribution'.

Quite extensive package for maximum likelihood estimation and weighted least squares estimation of categorical marginal models (CMMs; e.g., Bergsma and Rudas, 2002, <http://www.jstor.org/stable/2700006?; Bergsma, Croon and Hagenaars, 2009, <DOI:10.1007/b12532>.

Iterate and repel visually similar colors away in various ggplot2 plots. When many groups are plotted at the same time on multiple axes, for instance stacked bars or scatter plots, effectively ordering colors becomes difficult. This tool iterates through color combinations to find the best solution to maximize visual distinctness of nearby groups, so plots are more friendly toward colorblind users. This is achieved by two distance measurements, distance between groups within the plot, and CIELAB color space distances between colors as described in Carter et al., (2018) <doi:10.25039/TR.015.2018>.

Easy way to draw chronological charts from tables, aiming to include an intuitive environment for anyone new to R. Includes ggplot2 geoms and theme for chronological charts.

Predicts 3 to 12 months prognosis in Chronic Obstructive Pulmonary Disease (COPD) patients hospitalized for severe exacerbations, as described in Almagro et al. (2014) <doi:10.1378/chest.13-1328>.

Utilizes spatial association marginal contributions derived from spatial stratified heterogeneity to capture the degree of correlation between spatial patterns.

Single objective optimization using a CMA-ES.

Composite Kernel Machine Regression based on Likelihood Ratio Test (CKLRT): in this package, we develop a kernel machine regression framework to model the overall genetic effect of a SNP-set, considering the possible GE interaction. Specifically, we use a composite kernel to specify the overall genetic effect via a nonparametric function and we model additional covariates parametrically within the regression framework. The composite kernel is constructed as a weighted average of two kernels, one corresponding to the genetic main effect and one corresponding to the GE interaction effect. We propose a likelihood ratio test (LRT) and a restricted likelihood ratio test (RLRT) for statistical significance. We derive a Monte Carlo approach for the finite sample distributions of LRT and RLRT statistics. (N. Zhao, H. Zhang, J. Clark, A. Maity, M. Wu. Composite Kernel Machine Regression based on Likelihood Ratio Test with Application for Combined Genetic and Gene-environment Interaction Effect (Submitted).).

Create contour lines for a non regular series of points, potentially from a non-regular canvas.

Network meta-analysis and meta-regression (allows including up to three covariates) for individual participant data, aggregate data, and mixtures of both formats using the three-level hierarchical model. Each format can come from randomized controlled trials or non-randomized studies or mixtures of both. Estimates are generated in a Bayesian framework using JAGS. The implemented models are described by Hamza et al. 2023 <DOI:10.1002/jrsm.1619>.

This package provides equations commonly used in clinical pharmacokinetics and clinical pharmacology, such as equations for dose individualization, compartmental pharmacokinetics, drug exposure, anthropomorphic calculations, clinical chemistry, and conversion of common clinical parameters. Where possible and relevant, it provides multiple published and peer-reviewed equations within the respective R function.

Herramientas para el análisis de datos de COVID-19 en México. Descarga y analiza los datos para COVID-19 de la Direccion General de Epidemiologà a de México (DGE) <https://www.gob.mx/salud/documentos/datos-abiertos-152127>, la Red de Infecciones Respiratorias Agudas Graves (Red IRAG) <https://www.gits.igg.unam.mx/red-irag-dashboard/reviewHome> y la Iniciativa Global para compartir todos los datos de influenza (GISAID) <https://gisaid.org/>. English: Downloads and analyzes data of COVID-19 from the Mexican General Directorate of Epidemiology (DGE), the Network of Severe Acute Respiratory Infections (IRAG network),and the Global Initiative on Sharing All Influenza Data GISAID.