The Biodem package provides a number of functions for Biodemographic analysis.

Sample dataframes by group, in the form of a block bootstrap'. Entire groups are returned allowing for a single observation to span multiple rows of the dataframe.

The binomialRF is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, binomialRF then tests whether a feature is selected more often than by random chance.

Whitening is the first step of almost all blind source separation (BSS) methods. A fast implementation of whitening for BSS is implemented to serve as a lightweight dependency for packages providing BSS methods.

This package provides comprehensive tools for Bayesian model diagnostics and comparison. Includes prior sensitivity analysis, posterior predictive checks (Gelman et al. (2013) <doi:10.1201/b16018>), advanced model comparison using Pareto-smoothed importance sampling leave-one-out cross-validation (Vehtari et al. (2017) <doi:10.1007/s11222-016-9696-4>), convergence diagnostics, and prior elicitation tools. Integrates with brms (Burkner (2017) <doi:10.18637/jss.v080.i01>), rstan', and rstanarm packages for comprehensive Bayesian workflow diagnostics.

Easy estimation of Bayesian multilevel mediation models with Stan.

Connect to the D2L Brightspace Data Sets ('BDS') API via OAuth2', download all available datasets as tidy data frames with proper types, join them using convenience functions that know the foreign key relationships, and analyse student engagement, performance, and retention with ready-made analytics functions.

This package provides a client for retrieving data and metadata from major central bank APIs. It supports access to the Bundesbank SDMX Web Service API (<https://www.bundesbank.de/en/statistics/time-series-databases/help-for-sdmx-web-service/web-service-interface-data>), the Swiss National Bank Data Portal (<https://data.snb.ch/en>), the European Central Bank Data Portal API (<https://data.ecb.europa.eu/help/api/overview>), the Bank of England Interactive Statistical Database (<https://www.bankofengland.co.uk/boeapps/database>), the Banco de España API (<https://www.bde.es/webbe/en/estadisticas/recursos/api-estadisticas-bde.html>), the Bank for International Settlements SDMX Web Service (<https://stats.bis.org/api-doc/v1/>), the Banque de France Web Service (<https://webstat.banque-france.fr/en/pages/guide-migration-api/>), the Norges Bank SDMX Web Service (<https://www.norges-bank.no/en/topics/Statistics/open-data/>), the Oesterreichische Nationalbank Web Service (<https://www.oenb.at/en/Statistics/User-Defined-Tables/webservice.html>), and Bank of Canada Valet API (<https://www.bankofcanada.ca/valet/docs>).

Exposes the binary search functions of the C++ standard library (std::lower_bound, std::upper_bound) plus other convenience functions, allowing faster lookups on sorted vectors.

This package provides a collection of functions to test spatial autocorrelation between variables, including Moran I, Geary C and Getis G together with scatter plots, functions for mapping and identifying clusters and outliers, functions associated with the moments of the previous statistics that will allow testing whether there is bivariate spatial autocorrelation, and a function that allows identifying (visualizing neighbours) on the map, the neighbors of any region once the scheme of the spatial weights matrix has been established.

Adjusts longitudinal regression models using Bayesian methodology for covariance structures of composite symmetry (SC), autoregressive ones of order 1 AR (1) and autoregressive moving average of order (1,1) ARMA (1,1).

R client for Bender Hyperparameters optimizer : <https://bender.dreem.com> The R client allows you to communicate with the Bender API and therefore submit some new trials within your R script itself.

Evaluates the probability density function, cumulative distribution function, quantile function, random numbers, survival function, hazard rate function, and maximum likelihood estimates for the following distributions: Bell exponential, Bell extended exponential, Bell Weibull, Bell extended Weibull, Bell-Fisk, Bell-Lomax, Bell Burr-XII, Bell Burr-X, complementary Bell exponential, complementary Bell extended exponential, complementary Bell Weibull, complementary Bell extended Weibull, complementary Bell-Fisk, complementary Bell-Lomax, complementary Bell Burr-XII and complementary Bell Burr-X distribution. Related work includes: a) Fayomi A., Tahir M. H., Algarni A., Imran M. and Jamal F. (2022). "A new useful exponential model with applications to quality control and actuarial data". Computational Intelligence and Neuroscience, 2022. <doi:10.1155/2022/2489998>. b) Alanzi, A. R., Imran M., Tahir M. H., Chesneau C., Jamal F. Shakoor S. and Sami, W. (2023). "Simulation analysis, properties and applications on a new Burr XII model based on the Bell-X functionalities". AIMS Mathematics, 8(3): 6970-7004. <doi:10.3934/math.2023352>. c) Algarni A. (2022). "Group Acceptance Sampling Plan Based on New Compounded Three-Parameter Weibull Model". Axioms, 11(9): 438. <doi:10.3390/axioms11090438>.

This package provides a Bayesian variable selection approach using continuous spike and slab prior distributions. The prior choices here are motivated by the shrinking and diffusing priors studied in Narisetty & He (2014) <DOI:10.1214/14-AOS1207>.

The main functions carry out Gibbs sampler routines for nonparametric and semiparametric Bayesian models for random effects meta-analysis.

These are miscellaneous functions for working with panel data, quantiles, and printing results. For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance. For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions. Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas.

Data files and functions accompanying the book Korner-Nievergelt, Roth, von Felten, Guelat, Almasi, Korner-Nievergelt (2015) "Bayesian Data Analysis in Ecology using R, BUGS and Stan", Elsevier, New York.

Read and process brand.yml YAML files. brand.yml is a simple, portable YAML file that codifies your company's brand guidelines into a format that can be used by Quarto', Shiny and R tooling to create branded outputs. Maintain unified, branded theming for web applications to printed reports to dashboards and presentations with a consistent look and feel.

Parse and read the files that comply with the brain imaging data structure, or BIDS format, see the publication from Gorgolewski, K., Auer, T., Calhoun, V. et al. (2016) <doi:10.1038/sdata.2016.44>. Provides query functions to extract and check the BIDS entity information (such as subject, session, task, etc.) from the file paths and suffixes according to the specification. The package is developed and used in the reproducible analysis and visualization of intracranial electroencephalography, or RAVE', see Magnotti, J. F., Wang, Z., and Beauchamp, M. S. (2020) <doi:10.1016/j.neuroimage.2020.117341>; see citation("bidsr") for details and attributions.

This package provides functionality for determining the sample size of replication studies using Bayesian design approaches in the normal-normal hierarchical model (Pawel et al., 2022) <doi:10.48550/arXiv.2211.02552>.

Beta version of Bayesian Inference (BI) using python and BI. It aims to unify the modeling experience by providing an intuitive model-building syntax together with the flexibility of low-level abstraction coding. It also includes pre-built functions for high-level abstraction and supports hardware-accelerated computation for improved scalability, including parallelization, vectorization, and execution on CPU, GPU, or TPU.

Extend lasso and elastic-net model fitting for large data sets that cannot be loaded into memory. Designed to be more memory- and computation-efficient than existing lasso-fitting packages like glmnet and ncvreg', thus allowing the user to analyze big data with limited RAM <doi:10.32614/RJ-2021-001>.

This package implements Bayesian brain mapping with population-derived priors, including the original model described in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638>, the model with spatial priors described in Mejia et al. (2022) <doi:10.1080/10618600.2022.2104289>, and the model with population-derived priors on functional connectivity described in Mejia et al. (2025) <doi:10.1093/biostatistics/kxaf022>. Population-derived priors are based on templates representing established brain network maps, for example derived from independent component analysis (ICA), parcellations, or other methods. Model estimation is based on expectation-maximization or variational Bayes algorithms. Includes direct support for CIFTI', GIFTI', and NIFTI neuroimaging file formats.

This package provides tools for the calculation of common biodiversity indices from count data. Additionally, it incorporates bootstrapping techniques to generate multiple samples, facilitating the estimation of confidence intervals around these indices. Furthermore, the package allows for the exploration of how variation in these indices changes with differing numbers of sites, making it a useful tool with which to begin an ecological analysis. Methods are based on the following references: Chao et al. (2014) <doi:10.1890/13-0133.1>, Chao and Colwell (2022) <doi:10.1002/9781119902911.ch2>, Hsieh, Ma,` and Chao (2016) <doi:10.1111/2041-210X.12613>.