Generating population projections for all countries of the world using several probabilistic components, such as total fertility rate, life expectancy at birth and net migration (Raftery et al., 2012 <doi:10.1073/pnas.1211452109>). The package can be also used for subnational population projections.

This package provides functions for training extreme gradient boosting model using propensity score A-learning and weight-learning methods. For further details, see Liu et al. (2024) <doi:10.1093/bioinformatics/btae592>.

Build and compare nested statistical models with sets of equal and different independent variables. An analysis using this package is Marquardt et al. (2021) <https://github.com/p-mq/Percentile_based_averaging>.

Offers a flexible formula-based interface for building and training Bayesian Neural Networks powered by Stan'. The package supports modeling complex relationships while providing rigorous uncertainty quantification via posterior distributions. With features like user chosen priors, clear predictions, and support for regression, binary, and multi-class classification, it is well-suited for applications in clinical trials, finance, and other fields requiring robust Bayesian inference and decision-making. References: Neal(1996) <doi:10.1007/978-1-4612-0745-0>.

Fit Bayesian multivariate GARCH models using Stan for full Bayesian inference. Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, alongside a constant covariance baseline (that can be used for testing whether GARCH is warranted), based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) <doi:10.1198/073500102288618487> and Bollerslev (1990). The BEKK parameterization follows Engle and Kroner (1995) <doi:10.1017/S0266466600009063> while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) <doi:10.31234/osf.io/j57pk>. The fitted models contain rstan objects and can be examined with rstan functions.

Tool to find where a function has its lowest value(minimum). The functions can be any dimensions. Recommended use is with eps=10^-10, but can be run with 10^-20, although this depends on the function. Two more methods are in this package, simple gradient method (Gradmod) and Powell method (Powell). These are not recommended for use, their purpose are purely for comparison.

This package provides data import and offers 3 daily snapshot functions from securities of varying prices traded on the Bolivian Securities Exchange, website <https://www.bbv.com.bo/>. The snapshots include a detailed list, scatter plot correlation, and descriptive statistics table for the securities.

This package provides simple economic indicators for analyzing financial position time series of Islamic Commercial Banks in Indonesia.

Estimate fish length-at-age models using MCMC analysis with rstan models. This package allows a multimodel approach to growth fitting to be applied to length-at-age data and is supported by further analyses to determine model selection and result presentation. The core methods of this package are presented in Smart and Grammer (2021) "Modernising fish and shark growth curves with Bayesian length-at-age models". PLOS ONE 16(2): e0246734 <doi:10.1371/journal.pone.0246734>.

Fetches, harmonizes, and analyses data from the Spanish National Forest Inventory for reproducible, design-aware forest inventory workflows. Computes tree- and stand-level metrics, applies sampling-based expansion factors, estimates volume, and supports extensible processing for external inventory designs with custom sampling schemes and volume equations. Spatial extensions can attach plot geometries, preserve geometry sidecars through metric workflows, and return georeferenced sf outputs for mapping and remote-sensing integration.

This package provides methods for assessing animal movement from telemetry and biologging data using non-parametric Bayesian methods. This includes features for pre- processing and analysis of data, as well as the visualization of results from the models. This framework does not rely on standard parametric density functions, which provides flexibility during model fitting. Further details regarding part of this framework can be found in Cullen et al. (2022) <doi:10.1111/2041-210X.13745>.

Bayesian regularization for feed-forward neural networks.

This package contains functions for evaluating, analyzing, and fitting combined action dose response surfaces with the Bivariate Response to Additive Interacting Doses (BRAID) model of combined action, along with tools for implementing other combination analysis methods, including Bliss independence, combination index, and additional response surface methods.

This package provides tools for Markov chain Monte Carlo (MCMC) and Maximum A Posteriori (MAP) estimation utilizing the RTMB package. It supports various statistical models including generalized linear mixed models, factor analysis, item response theory, and multidimensional unfolding. The package allows users to easily transition between frequentist and Bayesian paradigms using a unified interface. Automatic differentiation and Laplace approximation follow Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>, and MCMC sampling uses the No-U-Turn Sampler described by Hoffman and Gelman (2014) <https://jmlr.org/papers/v15/hoffman14a.html>.

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>.

Efficiently access the Bedrock Bio library of open-access computational biology datasets. Lazily query datasets backed by DuckDB and Apache Iceberg', with support for predicate pushdown and column projection to the cloud storage backend. This enables quick, iterative access to otherwise massive, unwieldy datasets without downloading them in full. See <https://bedrock.bio> for available datasets and documentation.

Look up genes, variants and proteins from R, without writing a client for every biological web service. Each service gets one client that makes the request and returns a table. Parsing is a separate function that needs no network, so it can run on a saved response and be tested offline. Transport, retries, caching and error handling are left to the biohttp package. Dependencies for single services are optional, so you do not install what you will not use. The services covered include Ensembl', described in Dyer et al. (2025) <doi:10.1093/nar/gkae1071>, UniProt', in The UniProt Consortium (2025) <doi:10.1093/nar/gkae1010>, gnomAD', in Chen et al. (2024) <doi:10.1038/s41586-023-06045-0>, Open Targets', in Buniello et al. (2025) <doi:10.1093/nar/gkae1128>, and the AlphaFold Protein Structure Database, in Varadi et al. (2024) <doi:10.1093/nar/gkad1011>. Each client's help page cites the service it calls.

Facilitates the importation of the Boston Blue Bike trip data since 2015. Functions include the computation of trip distances of given trip data. It can also map the location of stations within a given radius and calculate the distance to nearby stations. Data is from <https://www.bluebikes.com/system-data>.

An automated graphical exploratory data analysis (EDA) tool that introduces: a.) wideplot graphics for exploring the structure of a dataset through a grid of variables and graphic types. b.) longplot graphics, which present the entire catalog of available graphics for representing a particular variable using a grid of graphic types and variations on these types. c.) plotup function, which presents a particular graphic for a specific variable of a dataset. The plotup() function also makes it possible to obtain the code used to generate the graphic, meaning that the user can adjust its properties as needed. d.) matrixplot graphics that is a grid of a particular graphic showing bivariate relationships between all pairs of variables of a certain(s) type(s) in a multivariate data set.

Model-based clustering using Bayesian parsimonious Gaussian mixture models. MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection. GREEN et al. (1995) <doi:10.1093/biomet/82.4.711>.

This package provides a tabular data manipulation, exploration and validation toolkit with a base R-style interface (subset, transform, aggregate, merge, split) and no external computation dependency. Grouping, joins, ordering, filtering, reshaping and delimited-file reading run in a bundled C++ engine that uses multiple threads for the heavier operations. Grouped reducers accumulate in compiled code without materialising intermediate columns, so grouped aggregation and counting allocate close to nothing. Results are returned as an ordinary data frame with a light basetable class.

This package provides functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks <doi:10.1093/bioinformatics/btq124>.

Create life tables with a Bayesian approach, which can be very useful for modelling a complex health process when considering multiple predisposing factors and multiple coexisting health conditions. Details for this method can be found in: Lynch, Scott, et al., (2022) <doi:10.1177/00811750221112398>; Zang, Emma, et al., (2022) <doi:10.1093/geronb/gbab149>.

This package provides functions for bootstrapping with multilevel data and models (and mixed-effect models). It implements multiple bootstrap methods under the parametric, residual, and case bootstrap categories, as discussed in Van der Leeden, Meijer, and Busing (2008) <doi:10.1007/978-0-387-73186-5_11> and Carpenter, Goldstein, and Rasbash (2003) <doi:10.1111/1467-9876.00415>. Currently it supports fitted objects from the lme4 package.