Perform variable selection for Cox regression model with interval-censored data. Can deal with both low-dimensional and high-dimensional data. Case-cohort design can be incorporated. Two sets of covariates scenario can also be considered. The references are listed in the URL below.

This package provides a framework for specifying and running flexible linear-time reachability-based algorithms for graphical causal inference. Rule tables are used to encode and customize the reachability algorithm to typical causal and probabilistic reasoning tasks such as finding d-connected nodes or more advanced applications. For more information, see Wienöbst, Weichwald and Henckel (2025) <doi:10.48550/arXiv.2506.15758>.

This package implements the count splitting methodology from Neufeld et al. (2022) <doi:10.1093/biostatistics/kxac047> and Neufeld et al. (2023) <arXiv:2307.12985>. Intended for turning a matrix of single-cell RNA sequencing counts, or similar count datasets, into independent folds that can be used for training/testing or cross validation. Assumes that the entries in the matrix are from a Poisson or a negative binomial distribution.

This package implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2022) <doi:10.1111/biom.13465> for transformation-free linear regression for compositional outcomes and predictors.

Enumerate orientation-consistent directed networks from an undirected or partially directed skeleton, detect feedback loops, summarize topology, and simulate node dynamics via stochastic differential equations.

The CalMaTe method calibrates preprocessed allele-specific copy number estimates (ASCNs) from DNA microarrays by controlling for single-nucleotide polymorphism-specific allelic crosstalk. The resulting ASCNs are on average more accurate, which increases the power of segmentation methods for detecting changes between copy number states in tumor studies including copy neutral loss of heterozygosity. CalMaTe applies to any ASCNs regardless of preprocessing method and microarray technology, e.g. Affymetrix and Illumina.

This package provides tools for producing data-driven curriculum vitae documents from structured data stored in an Excel workbook. The core workflow reads CV content from a workbook, converts it into Typst layout blocks, and renders a polished PDF via the Quarto publishing system. Includes functions for reading and cleaning CV data, building Typst section headings and entries, rendering CV sections from data frames, and scaffolding new CV projects with a standard folder structure and template workbook. Designed to separate content from layout: CV data lives in the spreadsheet, rendering configuration lives in Quarto', and transformation logic lives in small, reusable R functions. See the Typst typesetting system at <https://typst.app> and the Quarto publishing system at <https://quarto.org>. Inspired by the vitae package <https://CRAN.R-project.org/package=vitae> and the Awesome CV LaTeX template <https://github.com/posquit0/Awesome-CV>.

The phenology of plants (i.e. the timing of their annual life phases) depends on climatic cues. For temperate trees and many other plants, spring phases, such as leaf emergence and flowering, have been found to result from the effects of both cool (chilling) conditions and heat. Fruit tree scientists (pomologists) have developed some metrics to quantify chilling and heat (e.g. see Luedeling (2012) <doi:10.1016/j.scienta.2012.07.011>). chillR contains functions for processing temperature records into chilling (Chilling Hours, Utah Chill Units and Chill Portions) and heat units (Growing Degree Hours). Regarding chilling metrics, Chill Portions are often considered the most promising, but they are difficult to calculate. This package makes it easy. chillR also contains procedures for conducting a PLS analysis relating phenological dates (e.g. bloom dates) to either mean temperatures or mean chill and heat accumulation rates, based on long-term weather and phenology records (Luedeling and Gassner (2012) <doi:10.1016/j.agrformet.2011.10.020>). As of version 0.65, it also includes functions for generating weather scenarios with a weather generator, for conducting climate change analyses for temperature-based climatic metrics and for plotting results from such analyses. Since version 0.70, chillR contains a function for interpolating hourly temperature records.

This package provides a toolkit for querying Team Cymru <http://team-cymru.org> IP address, Autonomous System Number ('ASN'), Border Gateway Protocol ('BGP'), Bogon and Malware Hash Data Services.

This package provides a comprehensive interface to access diverse public data about Colombia through multiple APIs and curated datasets. The package integrates four different APIs: API-Colombia for Colombian-specific data including geography, culture, tourism, and government information; World Bank API for economic and demographic indicators; Nager.Date for public holidays; and REST Countries API for general country information. The package enables users to explore various aspects of Colombia such as geographic locations, cultural attractions, economic indicators, demographic data, and public holidays. Additionally, ColombiAPI includes curated datasets covering Bogota air stations, business and holiday dates, public schools, Colombian coffee exports, cannabis licenses, Medellin rainfall, malls in Bogota, as well as datasets on indigenous languages, student admissions and school statistics, forest liana mortality, municipal and regional data, connectivity and digital infrastructure, program graduates, vehicle counts, international visitors, and GDP projections. These datasets provide users with a rich and multifaceted view of Colombian social, economic, environmental, and technological information, making ColombiAPI a comprehensive tool for exploring Colombia's diverse data landscape. For more information on the APIs, see: API-Colombia <https://api-colombia.com/>, Nager.Date <https://date.nager.at/Api>, World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392>, and REST Countries API <https://restcountries.com/>.

Statistical downscaling and bias correction (model output statistics) method based on cumulative distribution functions (CDF) transformation. See Michelangeli, Vrac, Loukos (2009) Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophysical Research Letters, 36, L11708, <doi:10.1029/2009GL038401>. ; and Vrac, Drobinski, Merlo, Herrmann, Lavaysse, Li, Somot (2012) Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment. Nat. Hazards Earth Syst. Sci., 12, 2769-2784, www.nat-hazards-earth-syst-sci.net/12/2769/2012/, <doi:10.5194/nhess-12-2769-2012>.

Tables summarizing clinical trial results are often complex and require detailed tailoring prior to submission to a health authority. The crane package supplements the functionality of the gtsummary package for creating these often highly bespoke tables in the pharmaceutical industry.

This package creates an HTML vertical timeline from a data frame as an input for rmarkdown documents and shiny applications.

With this package you can run ConMET locally in R. ConMET is an R-shiny application that facilitates performing and evaluating confirmatory factor analyses (CFAs) and is useful for running and reporting typical measurement models in applied psychology and management journals. ConMET automatically creates, compares and summarizes CFA models. Most common fit indices (E.g., CFI and SRMR) are put in an overview table. ConMET also allows to test for common method variance. The application is particularly useful for teaching and instruction of measurement issues in survey research. The application uses the lavaan package (Rosseel, 2012) to run CFAs.

It is assumed that psychological distances between the categories are equal for the measurement instruments consisted of polytomously scored items. According to Muraki, this assumption must be tested. In the examination process of this assumption, the fit indexes are obtained and evaluated. This package provides that this assumption is removed. By with this package, the converted scale values of all items in a measurement instrument can be calculated by estimating a category parameter set for each item. Thus, the calculations can be made without any need to usage of the common category parameter set. Through this package, the psychological distances of the items are scaled. The scaling of a category parameter set for each item cause differentiation of score of the categories will be got from items. Also, the total measurement instrument score of an individual can be calculated according to the scaling of item score categories by with this package.This package provides that the place of individuals related to the structure to be measured with a measurement instrument consisted of polytomously scored items can be reveal more accurately. In this way, it is thought that the results obtained about individuals can be made more sensitive, and the differences between individuals can be revealed more accurately. On the other hand, it can be argued that more accurate evidences can be obtained regarding the psychometric properties of the measurement instruments.

Computed tomography (CT) imaging is a powerful tool for understanding the composition of sediment cores. This package streamlines and accelerates the analysis of CT data generated in the context of environmental science. Included are tools for processing raw DICOM images to characterize sediment composition (sand, peat, etc.). Root analyses are also enabled, including measures of external surface area and volumes for user-defined root size classes. For a detailed description of the application of computed tomography imaging for sediment characterization, see: Davey, E., C. Wigand, R. Johnson, K. Sundberg, J. Morris, and C. Roman. (2011) <DOI: 10.1890/10-2037.1>.

Case-based reasoning is a problem-solving methodology that involves solving a new problem by referring to the solution of a similar problem in a large set of previously solved problems. The key aspect of Case Based Reasoning is to determine the problem that "most closely" matches the new problem at hand. This is achieved by defining a family of distance functions and using these distance functions as parameters for local averaging regression estimates of the final result. The optimal distance function is chosen based on a specific error measure used in regression estimation. This approach allows for efficient problem-solving by leveraging past experiences and adapting solutions from similar cases. The underlying concept is inspired by the work of Dippon J. et al. (2002) <doi:10.1016/S0167-9473(02)00058-0>.

Deconvolution of bulk RNA-Sequencing data into proportions of cells based on a reference single-cell RNA-Sequencing dataset using high-dimensional geometric methodology <doi:10.64898/2026.01.24.701240>.

Quickly and easily create codebooks (i.e. data dictionaries) directly from a data frame.

An algorithm developed to efficiently and accurately process complex and variable cardiac data with three key features: 1. employing autocorrelation to identify recurrent heartbeats and use their periods to compute heart rates; 2. incorporating a genetic algorithm framework to minimize data loss due to noise interference and accommodate within-sequence variations; and 3. introducing a tracking index as a moving reference to reduce errors. Lau, Wong, & Gu (2026) <https://ssrn.com/abstract=5153081>.

Jointly model the accuracy of cognitive responses and item choices within a Bayesian hierarchical framework as described by Culpepper and Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package contains the datasets used within the analysis of the paper.

Linear or nonlinear cross-lagged panel model can be built from input data. Users can choose the appropriate method from three methods for constructing nonlinear cross lagged models. These three methods include polynomial regression, generalized additive model and generalized linear mixed model.In addition, a function for determining linear relationships is provided. Relevant knowledge of cross lagged models can be learned through the paper by Fredrik Falkenström (2024) <doi:10.1016/j.cpr.2024.102435> and the paper by A Gasparrini (2010) <doi:10.1002/sim.3940>.

Nonparametric rank based tests (rank-sum tests and signed-rank tests) for clustered data, especially useful for clusters having informative cluster size and intra-cluster group size.

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 features joint count transformation models with covariate-dependent correlations applied to a species community of three aquatic birds <DOI:10.48550/arXiv.2201.13095>.