This package provides a container for data used by the usmap package. The data used by usmap has been extracted into this package so that the file size of the usmap package can be reduced greatly. The data in this package will be updated roughly once per year as new map data files are provided by the US Census Bureau.

This package provides a set of custom R Markdown templates for documents and presentations with the University of Illinois at Urbana-Champaign (UIUC) color scheme and identity standards.

This package provides a streamlined workflow for UK Biobank cloud-based analysis on the Research Analysis Platform (RAP). Includes tools for phenotype extraction and decoding, variable derivation, survival and association analysis, genetic risk score computation, and publication-quality visualization. For details on the UK Biobank resource, see Bycroft et al. (2018) <doi:10.1038/s41586-018-0579-z>.

This package implements the U-smile methodology for threshold-free, class-specific comparison of probabilistic binary classifiers. The package quantifies prediction improvement and worsening separately for non-events and events using the Brier alteration (BA), relative Brier (RB), improvement proportion (I) coefficients, and relative likelihood ratio (rLR) coefficients, and provides U-smile, prediction improvement-worsening, receiver operating characteristic, and precision-recall plots. The original U-smile framework is described in Kubiak et al. (2024) <doi:10.1371/journal.pone.0303276>, its three-level extension for imbalanced binary classification in Wieckowska et al. (2025) <doi:10.1371/journal.pone.0321661>, and the likelihood-based extension in Wieckowska and Guzik (2026) <doi:10.1038/s41598-026-40545-z>.

This package provides a unified R6-based interface for various machine learning models with automatic interface detection, consistent cross-validation, model interpretations via numerical derivatives, and visualization. Supports both regression and classification tasks with any model function that follows R's standard modeling conventions (formula or matrix interface).

Maximum likelihood estimation of univariate Gaussian Mixture Autoregressive (GMAR), Student's t Mixture Autoregressive (StMAR), and Gaussian and Student's t Mixture Autoregressive (G-StMAR) models, quantile residual tests, graphical diagnostics, forecast and simulate from GMAR, StMAR and G-StMAR processes. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) <doi:10.1111/jtsa.12108>, Mika Meitz, Daniel Preve, Pentti Saikkonen (2023) <doi:10.1080/03610926.2021.1916531>, Savi Virolainen (2022) <doi:10.1515/snde-2020-0060>.

Full listing of UK baby names occurring more than three times per year between 1974 and 2020, and rankings of baby name popularity by decade from 1904 to 1994.

This package performs Bayesian point estimation using Lindley's Approximation (1980) <doi:10.1111/j.2517-6161.1980.tb01102.x> for arbitrary univariate probability distributions under numerous censoring and truncation schemes. Users supply the probability density function (PDF), cumulative distribution function (CDF), survival function, log-prior density, initial parameter vector, support bounds, and observed data; the package automatically computes Bayesian point estimates under various loss functions using Lindley's approximation. Supported schemes include complete data, right censoring, left censoring, interval censoring, random censoring, block random censoring, Type-I censoring, Type-II censoring, progressive Type-II censoring, progressive first failure censoring, joint Type-I censoring, joint Type-II censoring, balanced joint progressive Type-II censoring, hybrid censoring, hybrid Type-I censoring, hybrid Type-II censoring, Type-I hybrid censoring, Type-II progressively hybrid censoring, doubly Type-II censoring, middle censoring, right truncation, and left truncation. The package computes posterior expectations of arbitrary smooth functions, supports multiple loss functions (squared error loss function (SELF), weighted squared error loss function (WSELF), modified quadratic squared error loss function (MQSELF), precautionary loss function (PLF), entropy loss function (ELF), linear-exponential (LINEX), generalized entropy loss function (GELF), Kullback-Leibler loss function (K-Loss), and user-defined), provides model selection criteria (Akaike information criterion (AIC), Bayesian information criterion (BIC), corrected Akaike information criterion (AICc), Hannan-Quinn information criterion (HQIC), consistent Akaike information criterion (CAIC), Kullback information criterion (KIC)), goodness-of-fit statistics (Kolmogorov-Smirnov, Anderson-Darling, Cramer-von Mises, Watson, Chi-square), residual analysis (Cox-Snell, Martingale, Deviance, Pearson, Generalized, Randomized quantile), comprehensive visualization tools, prediction utilities, and simulation functions for benchmarking estimators. Methods are described in Lindley (1980) <doi:10.1111/j.2517-6161.1980.tb01102.x>, Tierney and Kadane (1986) <doi:10.2307/2234555>, Tierney, Kass, and Kadane (1989) <doi:10.2307/2335663>, Nagar, Kumar, and Krishna (2026) <doi:10.59467/IJASS.2026.22.1>, Goel, Kumar, and Krishna (2026, "Estimation in power Lindley distributions using balanced joint progressively Type-II censored data"), Wu and Kus (2009) <doi:10.1016/j.csda.2009.03.010>, Goel and Krishna (2026) <doi:10.1007/s13198-026-03208-w>, Balakrishnan and Aggarwala (2000, ISBN:978-1-4612-1334-5), Mondal and Kundu (2020) <doi:10.1080/03610926.2018.1554128>, Ding and Gui (2023) <doi:10.3390/math11092003>, Prajapati, Mitra, and Kundu (2019) <doi:10.1007/s13571-018-0167-0>, Yadav, Jaiswal, and Yadav (2026) <doi:10.1007/s11135-026-02647-8>, Iyer, Jammalamadaka, and Kundu (2008) <doi:10.1016/j.jspi.2007.03.062>, Banerjee and Kundu (2008) <doi:10.1109/TR.2008.916890>, and Kundu and Joarder (2006) <doi:10.1016/j.csda.2005.05.002>.

Downloads data from the UK Police public data API, the full docs of which are available at <https://data.police.uk/docs/>. Includes data on police forces and police force areas, crime reports, and the use of stop-and-search powers.

The boundaries for geographical units in the United States of America contained in this package include state, county, congressional district, and zip code tabulation area. Contemporary boundaries are provided by the U.S. Census Bureau (public domain). Historical boundaries for the years from 1629 to 2000 are provided form the Newberry Library's Atlas of Historical County Boundaries (licensed CC BY-NC-SA). Additional data is provided in the USAboundariesData package; this package provides an interface to access that data.

Estimate ambient vitamin D-effective or erythemal dose using ultraviolet radiation (UV) data from the TEMIS database, based on date and geographical location.

Fit a univariate-guided sparse regression (lasso), by a two-stage procedure. The first stage fits p separate univariate models to the response. The second stage gives more weight to the more important univariate features, and preserves their signs. Conveniently, it returns an objects that inherits from class glmnet', so that all of the methods for glmnet are available. See Chatterjee, Hastie and Tibshirani (2025) <doi:10.1162/99608f92.c79ff6db> for details.

Model data with a suspected clustering structure (either in co-variate space, regression space or both) using a Bayesian product model with a logistic regression likelihood. Observations are represented graphically and clusters are formed through various edge removals or additions. Cluster quality is assessed through the log Bayesian evidence of the overall model, which is estimated using either a Sequential Monte Carlo sampler or a suitable transformation of the Bayesian Information Criterion as a fast approximation of the former. The internal Iterated Batch Importance Sampling scheme (Chopin (2002 <doi:10.1093/biomet/89.3.539>)) is made available as a free standing function.

This package provides a time series of the national grid demand (high-voltage electric power transmission network) in the UK since 2011.

An engine for univariate time series forecasting using different regression models in an autoregressive way. The engine provides an uniform interface for applying the different models. Furthermore, it is extensible so that users can easily apply their own regression models to univariate time series forecasting and benefit from all the features of the engine, such as preprocessings or estimation of forecast accuracy.

Format text (bold, italic, ...) and numbers using UTF-8. Offers functions to search for emojis and include them in your text.

Assess essential unidimensionality using external validity information using the procedure proposed by Ferrando & Lorenzo-Seva (2019) <doi:10.1177/0013164418824755>. Provides two indices for assessing differential and incremental validity, both based on a second-order modelling schema for the general factor.

Distribution-independent framework for importance-sampling inference with univariate observations subject to censoring or truncation. Users provide probability functions and a proposal over model parameters. Constructs observed-data likelihood contributions, computes numerically stable importance weights, and supplies posterior, likelihood, predictive, diagnostic, and model-comparison summaries. Covers complete, right, left, interval, Type-I, Type-II, progressive Type-II, first-failure, progressive first-failure, doubly Type-II, middle-censored, and left/right-truncated data. Methods for importance sampling and censoring schemes are described in Geweke (1989) <doi:10.2307/2290062>, Hesterberg (1995) <doi:10.1080/00031305.1995.10476138>, Robert and Casella (2004, ISBN:978-0-387-21617-1), Kundu and Joarder (2006) <doi:10.1016/j.csda.2005.05.002>, Banerjee and Kundu (2008) <doi:10.1109/TR.2008.916890>, Iyer, Jammalamadaka, and Kundu (2008) <doi:10.1016/j.jspi.2007.03.062>, Wu and Kus (2009) <doi:10.1016/j.csda.2009.03.010>, Prajapati, Mitra, and Kundu (2019) <doi:10.1007/s13571-018-0167-0>, Mondal and Kundu (2020) <doi:10.1080/03610926.2018.1554128>, Balakrishnan and Aggarwala (2000, ISBN:980-1-4612-1334-5), Ding and Gui (2023) <doi:10.3390/math11092003>, Nagar, Kumar, and Krishna (2026) <doi:10.59467/IJASS.2026.22.1>, Goel and Krishna (2026) <doi:10.1007/s13198-026-03208-w>, Yadav, Jaiswal, and Yadav (2026) <doi:10.1007/s11135-026-02647-8>, and Goel, Kumar, and Krishna (2026, "Estimation in power Lindley distributions using balanced joint progressively Type-II censored data").

Make requests from the US Treasury Fiscal Data API endpoints.

Changes the column names of the inputted dataset to the correct names from the Uniform Crime Report codebook for the "Offenses Known and Clearance by Arrest" datasets from 1998-2014.

Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or self-defined penalties on the B-spline coefficients.

Fetch United States Congressional Records from their API <https://api.govinfo.gov/docs/> such as congressional speeches, speaker names, and metadata about congressional sessions, and detailed granule records. Optional parameters allow users to specify congressional sessions, and the maximum number of speeches to retrieve. Data is parsed, cleaned, and returned in a structured dataframe for analysis.

Does uniformly most powerful (UMP) and uniformly most powerful unbiased (UMPU) tests. At present only distribution implemented is binomial distribution. Also does fuzzy tests and confidence intervals (following Geyer and Meeden, 2005, <doi:10.1214/088342305000000340>) for the binomial distribution (one-tailed procedures based on UMP test and two-tailed procedures based on UMPU test).

When a package is loaded, the source repository is checked for new versions and a message is shown in the console indicating whether the package is out of date.