This package provides tidy, harmonized access to demographic data from the Spanish National Statistics Institute (INE) fenomenos demograficos domain, including population, births, and deaths, retrieved live via the official ineapir API wrapper, with optional spatial integration at municipality and province level via mapSpain'. Mortality indicators follow the Human Mortality Database Methods Protocol, including the average age at death in infancy method of Andreev and Kingkade (2015) <doi:10.4054/DemRes.2015.33.13>.

This is an Automatic Item Generator for Psychological Assessment. Items created with the IMak package should not be used in applied settings as part of the working protocol without ensuring first that the items meet the required psychometric quality standards (see Blum & Holling, 2018) <DOI:10.3389/fpsyg.2018.01286>.

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.

Interaction and analysis of multiple response data, along with other tools for analysing these types of data including missing value analysis and calculation of standard errors for a range of covariance matrix results (proportions, multinomial, independent samples, and multiple response).

The function install_load checks the local R library(ies) to see if the required package(s) is/are installed or not. If the package(s) is/are not installed, then the package(s) will be installed along with the required dependency(ies). This function pulls source or binary packages from the Posit/RStudio-sponsored CRAN mirror. Lastly, the chosen package(s) is/are loaded. The function load_package simply loads the provided package(s). If this package does not fit your needs, then you may want to consider these other R packages: needs', easypackages', pacman', pak', anyLib', and/or librarian'.

Currently used CI method has its limitation when the test statistics are asymmetrical (chi-square test, F-test) or the model functions are non-linear. It can be overcome by using the likelihood functions for the interval estimation. inteli package now supports interval estimation for the mean, variance, variance ratio, binomial distribution, Poisson distribution, odds ratio, risk difference, relative risk and their likelihood function plots. Testing functions are also provided.

Assist in the estimation of the Intraclass Correlation Coefficient (ICC) from variance components of a one-way analysis of variance and also estimate the number of individuals or groups necessary to obtain an ICC estimate with a desired confidence interval width.

This package provides user tokens for ICES web services that require authentication and authorization. Web services covered by this package are ICES VMS database, the ICES DATSU web services, and the ICES SharePoint site <https://www.ices.dk/data/tools/Pages/WebServices.aspx>.

This package implements the procedures suggested in Esarey and Sumner (2017) <http://justinesarey.com/interaction-overconfidence.pdf> for controlling the false discovery rate when constructing marginal effects plots for models with interaction terms.

This package contains implementations of the integrative Cox model with uncertain event times proposed by Wang, et al. (2020) <doi:10.1214/19-AOAS1287>, the regularized Cox cure rate model with uncertain event status proposed by Wang, et al. (2023) <doi:10.1007/s12561-023-09374-w>, and other survival analysis routines including the Cox cure rate model proposed by Kuk and Chen (1992) <doi:10.1093/biomet/79.3.531> via an EM algorithm proposed by Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x>, the regularized Cox cure rate model with elastic net penalty following Masud et al. (2018) <doi:10.1177/0962280216677748>.

Fast raking for survey weighting. The computational core is written in Rust for speed. Supports multiple raking variables, automatic variable selection, weight bounding, and comprehensive diagnostics.

Interface to India Meteorological Department (IMD) gridded daily rainfall (0.25 degree, 1901-present) and temperature (1.0 degree, 1951-present) binary data. Provides functions to download, read, extract by point or boundary, compute climate indices, perform trend analysis, and produce publication-quality maps with Survey of India approved boundaries.

Simulate and implement early phase two-stage adaptive dose-finding design for binary and quasi-continuous toxicity endpoints. See Chiuzan et al. (2018) for further reading <DOI:10.1080/19466315.2018.1462727>.

This package provides classes and methods for seismic data analysis. The base classes and methods are inspired by the python code found in the ObsPy python toolbox <https://github.com/obspy/obspy>. Additional classes and methods support data returned by web services provided by the EarthScope Consortium. <https://service.earthscope.org/>.

This package implements the Information Combination (IComb) approach proposed by Nguyen, Vahid and Wickramasuriya (2025)<https://www.monash.edu/business/ebs/research/publications/ebs/2025/wp11-2025.pdf> for hierarchical forecast reconciliation. The method combines information from base forecasts constructed using different information sets while ensuring coherence. It is implemented using a penalized regression-based framework.

This package provides functions to estimate the intrinsic dimension of a dataset via likelihood-based approaches. Specifically, the package implements the TWO-NN and Gride estimators and the Hidalgo Bayesian mixture model. In addition, the first reference contains an extended vignette on the usage of the TWO-NN and Hidalgo models. References: Denti (2023, <doi:10.18637/jss.v106.i09>); Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>); Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).

For a list of specified package, we automate recursively determining and installing the missing CRAN package dependencies ("Depends", "Imports", "LinkingTo"). The main mechanisms used are tools::package_dependencies() and utils::install.packages().

An R client for the ipbase.com IP Geolocation API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://ipbase.com/docs> .

This package implements some item response models for multiple ratings, including the hierarchical rater model, conditional maximum likelihood estimation of linear logistic partial credit model and a wrapper function to the commercial FACETS program. See Robitzsch and Steinfeld (2018) for a description of the functionality of the package. See Wang, Su and Qiu (2014; <doi:10.1111/jedm.12045>) for an overview of modeling alternatives.

This package provides a model that provides researchers with a powerful tool for the classification and study of native corn by aiding in the identification of racial complexes which are fundamental to Mexico's agriculture and culture. This package has been developed based on data collected by "Proyecto Global de Maà ces Nativos México", which has conducted exhaustive surveys across the country to document the qualitative and quantitative characteristics of different types of native maize. The trained model uses a robust and diverse dataset, enabling it to achieve an 80% accuracy in classifying maize racial complexes. The characteristics included in the analysis comprise geographic location, grain and cob colors, as well as various physical measurements, such as lengths and widths.

This package implements the conditional inference forest approach to modeling interval-censored survival data. It also provides functions to tune the parameters and evaluate the model fit. See Yao et al. (2019) <arXiv:1901.04599>.

Facilitates access to the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, a comprehensive global inventory of species at risk of extinction. This package streamlines the process of determining conservation status by matching species names with Red List data, providing tools to easily query and retrieve conservation statuses. Designed to support biodiversity research and conservation planning, this package relies on data from the iucnrdata package, available on GitHub <https://github.com/PaulESantos/iucnrdata>. To install the data package, use pak::pak('PaulESantos/iucnrdata').

Reports and plots the conditional effect of each variable involved in a multiplicative interaction across the range of its moderators, together with the region over which that effect is distinguishable from zero. Extends the classic framework of Johnson and Neyman (1936) and Johnson and Fay (1950) <doi:10.1007/BF02288864> to three-way interactions and to Bayesian models. The single entry point JN() dispatches on the fitted object, with methods for lm()/glm() models, lme4 models, RSiena and multiSiena results, and matrices of posterior draws; support for further model classes is added by writing one jn_input() method. Results are classed objects with print(), summary() and plot() methods, and the figures carry data-density panels showing how much empirical support each part of the moderator range has. A detailed introduction can be found in Krause (2026) <doi:10.48550/arXiv.2604.22051>.

Get image statistics based on processing fluency theory. The functions provide scores for several basic aesthetic principles that facilitate fluent cognitive processing of images: contrast, complexity / simplicity, self-similarity, symmetry, and typicality. See Mayer & Landwehr (2018) <doi:10.1037/aca0000187> and Mayer & Landwehr (2018) <doi:10.31219/osf.io/gtbhw> for the theoretical background of the methods.