Tidyverse'-friendly interface to the Brazilian Institute of Geography and Statistics ('IBGE') aggregate data API <https://servicodados.ibge.gov.br/api/docs/agregados?versao=3>. Query aggregates, variables, localities, periods, and metadata from surveys and censuses conducted by IBGE'.

This package contains functions that allow Bayesian inference on a parameter of some widely-used exponential models. The functions can generate independent samples from the closed-form posterior distribution using the inverse stable prior. Inverse stable is a non-conjugate prior for a parameter of an exponential subclass of discrete and continuous data distributions (e.g. Poisson, exponential, inverse gamma, double exponential (Laplace), half-normal/half-Gaussian, etc.). The prior class provides flexibility in capturing a wide array of prior beliefs (right-skewed and left-skewed) as modulated by a parameter that is bounded in (0,1). The generated samples can be used to simulate the prior and posterior predictive distributions. More details can be found in Cahoy and Sedransk (2019) <doi:10.1007/s42519-018-0027-2>. The package can also be used as a teaching demo for introductory Bayesian courses.

This package provides a tool to calculate the performance of a time series in a specific date or period. It is more intended for data analysis in the fields of finance, banking, telecommunications or operational marketing.

This package provides a collection of intuitive and user-friendly functions for computing confidence intervals for common statistical tasks, including means, differences in means, proportions, and odds ratios. The package also includes tools for linear regression analysis and several real-world datasets intended for teaching and applied statistical inference.

This package contains datasets and several smaller functions suitable for analysis of interval-censored data. The package complements the book Bogaerts, Komárek and Lesaffre (2017, ISBN: 978-1-4200-7747-6) "Survival Analysis with Interval-Censored Data: A Practical Approach" <https://www.routledge.com/Survival-Analysis-with-Interval-Censored-Data-A-Practical-Approach-with/Bogaerts-Komarek-Lesaffre/p/book/9781420077476>. Full R code related to the examples presented in the book can be found at <https://ibiostat.be/online-resources/icbook/supplemental>. Packages mentioned in the "Suggests" section are used in those examples.

This package performs inference with the lasso in Gaussian Graphical Models. The package consists of wrappers for functions from the hdi package.

An R6-based client to facilitate interaction with the Statistics Portugal (Instituto Nacional de Estatistica - INE) API (<https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_api&INST=322751522&xlang=en>).

This package implements a nonparametric maximum likelihood method for assessing potentially time-varying vaccine efficacy (VE) against SARS-CoV-2 infection under staggered enrollment and time-varying community transmission, allowing crossover of placebo volunteers to the vaccine arm. Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) <doi:10.1093/cid/ciab630>.

Facilitates fitting measurement error and missing data imputation models using integrated nested Laplace approximations, according to the method described in Skarstein, Martino and Muff (2023) <doi:10.1002/bimj.202300078>. See Skarstein and Muff (2024) <doi:10.48550/arXiv.2406.08172> for details on using the package.

This package provides tools for multivariate nonparametrics, as location tests based on marginal ranks, spatial median and spatial signs computation, Hotelling's T-test, estimates of shape are implemented.

Calculates point estimates and standard errors using replicate weights and plausible values for International Large-Scale Assessments (ILSA), including: means, proportions, quantiles, correlations, singlelevel regressions, and multilevel regressions.

Authentication can be the most difficult part about working with a new API. ibmAcousticR facilitates making a connection to the IBM Acoustic email campaign management API and executing various queries. The IBM Acoustic API documentation is available at <https://developer.ibm.com/customer-engagement/docs/>. This package is not supported by IBM'.

This package implements a wide range of metrics for measuring glucose control and glucose variability based on continuous glucose monitoring data. The list of implemented metrics is summarized in Rodbard (2009) <doi:10.1089/dia.2009.0015>. Additional visualization tools include time-series plots, lasagna plots and ambulatory glucose profile report.

Models, analyzes, and forecasts financial intraday signals. This package currently supports a univariate state-space model for intraday trading volume provided by Chen (2016) <doi:10.2139/ssrn.3101695>.

Visualize interactions between multiple experimental factors using interactive 3D surface plots powered by plotly'. Instead of examining combinatorial pairwise interaction plots, map factor combinations to response surfaces and use surface crossings as geometric indicators of interaction effects. Supports continuous, categorical, and mixed factor designs with automatic binning for continuous conditioning variables.

Interpreting the differences between mean scale scores across various forms of an assessment can be challenging. This difficulty arises from different mappings between raw scores and scale scores, complex mathematical relationships, adjustments based on judgmental procedures, and diverse equating functions applied to different assessment forms. An alternative method involves running simulations to explore the effect of incrementing raw scores on mean scale scores. The idmact package provides an implementation of this approach based on the algorithm detailed in Schiel (1998) <https://www.act.org/content/dam/act/unsecured/documents/ACT_RR98-01.pdf> which was developed to help interpret differences between mean scale scores on the American College Testing (ACT) assessment. The function idmact_subj() within the package offers a framework for running simulations on subject-level scores. In contrast, the idmact_comp() function provides a framework for conducting simulations on composite scores.

Incremental Multiple Correspondence Analysis and Principal Component Analysis.

Use R to make requests to the US Census Bureau's International Data Base API. Results are returned as R data frames. For more information about the IDB API, visit <https://www.census.gov/data/developers/data-sets/international-database.html>.

This resource provides tools to create, compare, and post-process spatial isotope assignment models of animal origin. It generates probability-of-origin maps for individuals based on user-provided tissue and environment isotope values (e.g., as generated by IsoMAP, Bowen et al. [2013] <doi:10.1111/2041-210X.12147>) using the framework established in Bowen et al. (2010) <doi:10.1146/annurev-earth-040809-152429>). The package isocat can then quantitatively compare and cluster these maps to group individuals by similar origin. It also includes techniques for applying four approaches (cumulative sum, odds ratio, quantile only, and quantile simulation) with which users can summarize geographic origins and probable distance traveled by individuals. Campbell et al. [2020] establishes several of the functions included in this package <doi:10.1515/ami-2020-0004>.

Interesting igraph datasets from Melanie Walsh's sample social network datasets repository <https://github.com/melaniewalsh/sample-social-network-datasets>.

Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools.

Calculate various information criteria in literature for "lm" and "glm" objects.

This package provides two- and three-way Johnson-Neyman-(Krause) plots for easier interpretation of interactions. It extends the classic framework of Johnson and Neyman (1936) and Johnson and Fay (1950) <doi:10.1007/BF02288864> to Bayesian models and three-way interactions. The functions have dedicated routines for classic lm()/glm() models, as well as lme4 models and RSiena results. However, the package can also be used model agnostic and thus extends the availability of JN(K)-plots beyond what is currently available. A detailed introduction can be found in Krause (2026) <doi:10.48550/arXiv.2604.22051>.

Multi-data type subtyping, which is data type agnostic and accepts missing data. Subtyping is performed using intermediary assessments created with autoencoders and similarity calculations. See Fox et al. (2024) <doi:10.1016/j.crmeth.2024.100884> for details.