Calculates the credit debt for the next period based on the available data using the cross-classification credibility model.

Calculates the chilling and heat accumulation for studies of the temperate fruit trees. The models in this package are: Utah (Richardson et al., 1974, ISSN:0018-5345), Positive Chill Units - PCU (Linsley-Noaks et al., 1995, ISSN:1017-0316), GDH-A - Growing Degree Hours by Anderson et al.(1986, ISSN:0567-7572), GDH-R - Growing Degree Hours by Richardson et al.(1975, ISSN:0018-5345), North Carolina (Shaltout e Unrath, 1983, ISSN:0003-1062), Landsberg Model (Landsberg, 1974, ISSN:0305-7364), Q10 Model (Bidabe, 1967, ISSN:0031-9368), Jones Model (Jones et al., 2013 <DOI:10.1111/j.1438-8677.2012.00590.x>), Low-Chill Model (Gilreath and Buchanan, 1981, ISSN:0003-1062), Model for Cherry "Sweetheart" (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Model for apple "Gala" (Guak and Nielsen, 2013 <DOI:10.1007/s13580-013-0140-9>), Taiwan Model (Lu et al., 2012 <DOI:10.17660/ActaHortic.2012.962.35>), Dynamic Model (Fishman et al., 1987, ISSN:0022-5193) adapted from the function Dynamic_Model() of the chillR package (Luedeling, 2018), Unified Model (Chuine et al., 2016 <DOI:10.1111/gcb.13383>) and Heat Restriction model.

This package provides a reproducible framework for constructing and comparing gene regulatory networks by integrating chromatin accessibility footprint scores with matched RNA expression data. It implements context-specific enhancer-gene linking, transcription factor focused network analysis, differential network analysis, and regulatory topic modeling workflows for systematic exploration of gene regulation across conditions. Methodological background is described in Bentsen and others (2020) <doi:10.1038/s41467-020-18035-1>, Blei, Ng and Jordan (2003) <https://www.jmlr.org/papers/v3/blei03a.html>, and Chen, Li, Zhu and Chen (2015) <doi:10.48550/arXiv.1510.08628>.

Central limit theorem experiments presented by data frames or plots. Functions include generating theoretical sample space, corresponding probability, and simulated results as well.

This package performs simulation-based inference as an alternative to the delta method for obtaining valid confidence intervals and p-values for regression post-estimation quantities, such as average marginal effects and predictions at representative values. This framework for simulation-based inference is especially useful when the resulting quantity is not normally distributed and the delta method approximation fails. The methodology is described in Greifer, et al. (2025) <doi:10.32614/RJ-2024-015>. clarify is meant to replace some of the functionality of the archived package Zelig'; see the vignette "Translating Zelig to clarify" for replicating this functionality.

Statistical modeling for correlated count data using the beta-binomial distribution, described in Martin et al. (2020) <doi:10.1214/19-AOAS1283>. It allows for both mean and overdispersion covariates.

This package provides a covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. More details can be referred to Liu et al. (2024) <doi:10.1093/biomtc/ujae031>.

This package provides tools for constructing, labeling, and visualizing Causal Assignment Trees (CATs) in settings with staggered adoption. Supports Callaway and Sant'Anna difference-in-differences (CSDiD) and doubly robust difference-in-difference-differences (DR-DDD) designs. The package helps clarify treatment timing, never-treated vs. not-yet-treated composition, and subgroup structure, and produces publication-quality diagrams and summary tables. Current functionality focuses on data-to-node mapping, node counts, cohort-year summaries, and high-quality tree plots suitable for empirical applications prior to estimation. Methods are based on Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>, Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>, and Kilanko (2026) <https://github.com/VictorKilanko/catviz>.

Simulating bivariate survival data from various copula models. Estimating bivariate copula models with semiparametric or Weibull margins under various copulas. Two different ways to estimate the association parameter in copula models are implemented. A goodness-of-fit test for the Gumbel and Clayton copulas is also implemented for semiparametric models. See Emura, Lin and Wang (2010) <doi:10.1016/j.csda.2010.03.013> for details.

This package performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) <doi:10.1111/rssc.12599>). Also models the occurrence of radiocarbon samples as a variable-rate (inhomogeneous) Poisson process, plotting the posterior estimate for the occurrence rate of the samples over calendar time, and providing information about potential change points.

This package provides tools for detecting cellwise outliers and robust methods to analyze data which may contain them. Contains the implementation of the algorithms described in Rousseeuw and Van den Bossche (2018) <doi:10.1080/00401706.2017.1340909> (open access) Hubert et al. (2019) <doi:10.1080/00401706.2018.1562989> (open access), Raymaekers and Rousseeuw (2021) <doi:10.1080/00401706.2019.1677270> (open access), Raymaekers and Rousseeuw (2021) <doi:10.1007/s10994-021-05960-5> (open access), Raymaekers and Rousseeuw (2021) <doi:10.52933/jdssv.v1i3.18> (open access), Raymaekers and Rousseeuw (2022) <doi:10.1080/01621459.2023.2267777> (open access) Rousseeuw (2022) <doi:10.1016/j.ecosta.2023.01.007> (open access). Examples can be found in the vignettes: "DDC_examples", "MacroPCA_examples", "wrap_examples", "transfo_examples", "DI_examples", "cellMCD_examples" , "Correspondence_analysis_examples", and "cellwise_weights_examples".

Compare two classifications or clustering solutions that may or may not have the same number of classes, and that might have hard or soft (fuzzy, probabilistic) membership. Calculate various metrics to assess how the clusters compare to each other. The calculations are simple, but provide a handy tool for users unfamiliar with matrix multiplication. This package is not geared towards traditional accuracy assessment for classification/ mapping applications - the motivating use case is for comparing a probabilistic clustering solution to a set of reference or existing class labels that could have any number of classes (that is, without having to degrade the probabilistic clustering to hard classes).

Create descriptive tables for continuous and categorical variables. Apply summary statistics and counting function, with or without a grouping variable, and create beautiful reports using rmarkdown or officer'. You can also compute effect sizes and statistical tests if needed.

An experimental fork of stringr'. The functions and semantics are the same, apart from differences between ICU and Unicode versions, but everything is optimized around ALTREP strings (custom high-performance string storage), allowing for faster and more efficient operation.

Developed as a collaboration between Earth lab and the North Central Climate Adaptation Science Center to help users gain insights from available climate data. Includes tools and instructions for downloading climate data via a USGS API and then organizing those data for visualization and analysis that drive insight. Web interface for USGS API can be found at <http://thredds.northwestknowledge.net:8080/thredds/reacch_climate_CMIP5_aggregated_macav2_catalog.html>.

Imports conversation transcripts into R, concatenates them into a single dataframe appending event identifiers, cleans and formats the text, then yokes user-specified psycholinguistic database values to each word. ConversationAlign then computes alignment indices between two interlocutors across each transcript for >40 possible semantic, lexical, and affective dimensions. In addition to alignment, ConversationAlign also produces a table of analytics (e.g., token count, type-token-ratio) in a summary table describing your particular text corpus.

Implementation of two-dimensional (2D) correlation analysis based on the Fourier-transformation approach described by Isao Noda (I. Noda (1993) <DOI:10.1366/0003702934067694>). Additionally there are two plot functions for the resulting correlation matrix: The first one creates colored 2D plots, while the second one generates 3D plots.

Automated and robust framework for analyzing R-R interval (RRi) signals using advanced nonlinear modeling and preprocessing techniques. The package implements a dual-logistic model to capture the rapid drop and subsequent recovery of RRi during exercise, as described by Castillo-Aguilar et al. (2025) <doi:10.1038/s41598-025-93654-6>. In addition, CardioCurveR includes tools for filtering RRi signals using zero-phase Butterworth low-pass filtering and for cleaning ectopic beats via adaptive outlier replacement using local regression and robust statistics. These integrated methods preserve the dynamic features of RRi signals and facilitate accurate cardiovascular monitoring and clinical research.

Current-season color palettes for soccer clubs in the English Premier League ('EPL'), LaLiga', Serie A', the Bundesliga', Ligue 1 and Major League Soccer ('MLS'), and for Major League Baseball ('MLB'), National Basketball Association ('NBA'), Women's National Basketball Association ('WNBA'), National Hockey League ('NHL') and National Football League ('NFL') teams. Palettes are returned as named character vectors of hex colors, and as ggplot2 colour and fill scales. The palettes shipped in earlier versions of the package remain available so that older figures stay reproducible.

This package implements the JSON, INI, YAML and TOML parser for R setting and writing of configuration file. The functionality of this package is similar to that of package config'.

This package contains functions for single-cell RNA sequencing data analysis. It provides core functionalities for clustering cells, identifying markers for predefined clusters, performing sub-clustering of major cell populations, and discovering markers within custom-selected subsets of cells. CelliVerse also includes methods for analyzing cluster similarity and generating intuitive visualizations. Designed to be independent of library size and other sample- or cell-level confounding effects, CelliVerse ensures reliable and interpretable results across a wide range of datasets. Fred Viole and David Nawrocki (2013, ISBN:1490523995). Csardi G, Nepusz T (2006). "The igraph software package for complex network research." InterJournal, Complex Systems, 1695. Adopted algorithms and sources are referenced in function document.

Compares two dataframes which have the same column structure to show the rows that have changed. Also gives a git style diff format to quickly see what has changed in addition to summary statistics.

Predicts Rasch item difficulty from item features (text embeddings from any model, template family, content metadata) by ridge regression (Hoerl and Kennard, 1970, <doi:10.1080/00401706.1970.10488634>) with honest out-of-sample uncertainty, uses the predictions as robust informative priors to calibrate new items from small pretest samples, in the spirit of using collateral information in calibration (Mislevy, Sheehan and Wingersky, 1993, <doi:10.1111/j.1745-3984.1993.tb00422.x>), plans the pretest sample size needed for a target precision, and diagnoses item families whose predictions cannot be trusted.

This package provides SPSS'- and SAS'-like output for cross tabulations of two categorical variables (CROSSTABS) and for hierarchical loglinear analyses of two or more categorical variables (LOGLINEAR). The methods are described in Agresti (2013, ISBN:978-0-470-46363-5), Ajzen & Walker (2021, ISBN:9780429330308), Field (2018, ISBN:9781526440273), Norusis (2012, ISBN:978-0-321-74843-0), Nussbaum (2015, ISBN:978-1-84872-603-1), Stevens (2009, ISBN:978-0-8058-5903-4), Tabachnik & Fidell (2019, ISBN:9780134790541), and von Eye & Mun (2013, ISBN:978-1-118-14640-8).