This package performs estimation, diagnostics, and visualization of conditional marginal effects and group average treatment effects of a treatment on an outcome across different values of a moderator. Optionally integrates with the mlr3extralearners package for additional machine learning backends compatible with the double machine learning estimators. mlr3extralearners is not on CRAN but can be obtained from <https://github.com/mlr-org/mlr3extralearners>.

Advanced fuzzy logic based techniques are implemented to compute the similarity among different objects or items. Typically, application areas consist of transforming raw data into the corresponding advanced fuzzy logic representation and determining the similarity between two objects using advanced fuzzy similarity techniques in various fields of research, such as text classification, pattern recognition, software projects, decision-making, medical diagnosis, and market prediction. Functions are designed to compute the membership, non-membership, hesitant-membership, indeterminacy-membership, and refusal-membership for the input matrices. Furthermore, it also includes a large number of advanced fuzzy logic based similarity measure functions to compute the Intuitionistic fuzzy similarity (IFS), Pythagorean fuzzy similarity (PFS), and Spherical fuzzy similarity (SFS) between two objects or items based on their fuzzy relationships. It also includes working examples for each function with sample data sets.

Biodiversity is a multifaceted concept covering different levels of organization from genes to ecosystems. iNEXT.3D extends iNEXT to include three dimensions (3D) of biodiversity, i.e., taxonomic diversity (TD), phylogenetic diversity (PD) and functional diversity (FD). This package provides functions to compute standardized 3D diversity estimates with a common sample size or sample coverage. A unified framework based on Hill numbers and their generalizations (Hill-Chao numbers) are used to quantify 3D. All 3D estimates are in the same units of species/lineage equivalents and can be meaningfully compared. The package features size- and coverage-based rarefaction and extrapolation sampling curves to facilitate rigorous comparison of 3D diversity across individual assemblages. Asymptotic 3D diversity estimates are also provided. See Chao et al. (2021) <doi:10.1111/2041-210X.13682> for more details.

This package provides user-friendly and configurable print debugging via a single function, ic(). Wrap an expression in ic() to print the expression, its value and (where available) its source location. Debugging output can be toggled globally without modifying code.

Missing values often occur in financial data due to a variety of reasons (errors in the collection process or in the processing stage, lack of asset liquidity, lack of reporting of funds, etc.). However, most data analysis methods expect complete data and cannot be employed with missing values. One convenient way to deal with this issue without having to redesign the data analysis method is to impute the missing values. This package provides an efficient way to impute the missing values based on modeling the time series with a random walk or an autoregressive (AR) model, convenient to model log-prices and log-volumes in financial data. In the current version, the imputation is univariate-based (so no asset correlation is used). In addition, outliers can be detected and removed. The package is based on the paper: J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on Signal Processing, vol. 67, no. 8, pp. 2159-2172. <doi:10.1109/TSP.2019.2899816>.

Convert between bookmaker odds and probabilities. Eight different algorithms are available, including basic normalization, Shin's method (Hyun Song Shin, (1992) <doi:10.2307/2234526>), and others.

Run quality checks on data sets using the same checks that are conducted on the ICES Data Submission Utility (DATSU) <https://datsu.ices.dk>.

Read data from LimeSurvey (<https://www.limesurvey.org/>) in a comfortable way. Heavily inspired by limer (<https://github.com/cloudyr/limer/>), which lacked a few comfort features for me.

Compute onestep and multistep time series forecasts for machine learning models.

Get open statistical data and metadata disseminated by the National Statistics Institute of Spain (INE). The functions return data frames with the requested information thanks to calls to the INE API <https://www.ine.es/dyngs/DAB/index.htm?cid=1100>.

One function to read files. One function to write files. One function to direct plots to screen or file. Automatic file format inference and directory structure creation.

The integrity package implements the IPD Integrity Tool, a structured and transparent framework for evaluating the integrity of individual participant data (IPD) from randomised trials (see Hunter et al. (2024) <doi:10.1002/jrsm.1738> and <doi:10.32614/RJ-2017-008>). It supports users to identify potential issues, such as unusual data patterns, implausible values, lack of expected correlations, date violations, and inconsistencies. The package provides reproducible workflows for screening, documenting and summarising integrity concerns, and may be applied by evidence synthesists, editors, and others to determine whether a randomised trial may be considered sufficiently trustworthy to contribute to the evidence base that informs policy and practice.

Allows for the non-parametric estimation of transition intensities in interval-censored multi-state models using the approach of Gomon and Putter (2024) <doi:10.48550/arXiv.2409.07176> or Gu et al. (2023) <doi:10.1093/biomet/asad073>.

This package provides functions read a dataframe containing one or more International Classification of Diseases Tenth Revision codes per subject. They return original data with injury categorizations and severity scores added.

Estimates regression coefficients in proportional hazards models for interval-censored multistate data. Individuals may be observed at irregular times and their states may be partially observed. Allowable transitions and transition-specific covariate effects can be specified. The numerical estimation is implemented in C++ using RcppArmadillo'. The method implemented in this package is described in You, Liu, and Krischer (2024) <doi:10.1002/sim.10079>.

This package implements continuous-time hidden Markov models (HMMs) to infer identity-by-descent (IBD) segments shared by two individuals. Supports two- and three-state models using single-nucleotide polymorphism (SNP) genotypes or genotype likelihoods. Provides posterior probabilities at each marker (forward-backward algorithm), prediction of IBD segments (Viterbi algorithm), and functions for visualising results. Supports both autosomal data and X-chromosomal data. The methodology and package are described in Vigeland et al. (2026) <doi:10.1016/j.fsigen.2025.103409>.

Classical Ising Model is a land mark system in statistical physics.The model explains the physics of spin glasses and magnetic materials, and cooperative phenomenon in general, for example phase transitions and neural networks.This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions. Utility functions for exact solutions are provided. Such as transfer matrix for 1D. Utility functions for exact solutions are provided. Example use cases are as follows: Measuring effective ergodicity and power-laws in so called functional-diffusion. Example usage contains parallel runs, fitting power-laws, finite size scaling, computing autocorrelation, uncertainty analysis and plotting utilities.

This package provides a data-driven projection-based method for estimating changepoints in high-dimensional time series. Multiple changepoints are estimated using a (wild) binary segmentation scheme.

This package implements network-aware instrumental variable regression for causal node discovery in high-dimensional settings with graph-structured exposures. Provides IVGL and IVGL-S estimators combining graph-Laplacian penalization with IV-based identification, including correction for invalid instruments via a sisVIVE-style update. Methods are described in Pal and Ghosh (2026) <doi:10.48550/arXiv.2604.24969>. The glmgraph package, required for the main estimators, is available at the additional repository <https://djghosh1123.r-universe.dev>.

Reconstruct birth-year specific probabilities of immune imprinting to influenza A, using the methods of Gostic et al. (2016) <doi:10.1126/science.aag1322>. Plot, save, or export the calculated probabilities for use in your own research. By default, the package calculates subtype-specific imprinting probabilities, but with user-provided frequency data, it is possible to calculate probabilities for arbitrary kinds of primary exposure to influenza A, including primary vaccination and exposure to specific clades, strains, etc.

Facilitates the calculation of validated pancreatic islet hormone-release indices from fasting and oral glucose tolerance test (OGTT) measurements. Provides beta-cell insulin release indices (including HOMA-beta, corrected insulin response, Stumvoll first-phase index, BIGTT-AIR, and disposition indices) as described in Madsen (2024) <doi:10.1038/s42255-024-01140-6>, alongside alpha-cell glucagon release and glucagon resistance indices derived from the glucagon-suppression and liver-alpha-cell-axis literature. Enables reproducible assessment of beta-cell and alpha-cell function for metabolic and endocrine research.

This package provides functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites [De Caceres & Legendre (2009) <doi:10.1890/08-1823.1>]. Also includes functions to measure species niche breadth using resource categories [De Caceres et al. (2011) <doi:10.1111/J.1600-0706.2011.19679.x>].

This package provides tools for assessment and quantification of individual identity information in animal signals. This package accompanies a research article by Linhart et al. (2019) <doi:10.1101/546143>: "Measuring individual identity information in animal signals: Overview and performance of available identity metrics".

Data sets and scripts for text examples and exercises in P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534.