Application of genome prediction for a continuous variable, focused on genotype by environment (GE) genomic selection models (GS). It consists a group of functions that help to create regression kernels for some GE genomic models proposed by Jarquà n et al. (2014) <doi:10.1007/s00122-013-2243-1> and Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>. Also, it computes genomic predictions based on Bayesian approaches. The prediction function uses an orthogonal transformation of the data and specific priors present by Cuevas et al. (2014) <doi:10.1534/g3.114.013094>.

Fully Bayesian Classification with a subset of high-dimensional features, such as expression levels of genes. The data are modeled with a hierarchical Bayesian models using heavy-tailed t distributions as priors. When a large number of features are available, one may like to select only a subset of features to use, typically those features strongly correlated with the response in training cases. Such a feature selection procedure is however invalid since the relationship between the response and the features has be exaggerated by feature selection. This package provides a way to avoid this bias and yield better-calibrated predictions for future cases when one uses F-statistic to select features.

The BioTIME database was first published in 2018 and inspired ideas, questions, project and research article. To make it even more accessible, an R package was created. The BioTIMEr package provides tools designed to interact with the BioTIME database. The functions provided include the BioTIME recommended methods for preparing (gridding and rarefaction) time series data, a selection of standard biodiversity metrics (including species richness, numerical abundance and exponential Shannon) alongside examples on how to display change over time. It also includes a sample subset of both the query and meta data, the full versions of which are freely available on the BioTIME website <https://biotime.st-andrews.ac.uk/home.php>.

This package provides a collection of quantitative tools for selecting mediating effects within exploratory Bayesian mediation models. The package accommodates both continuous and dichotomous outcomes, including the dependent variables and the mediators for identifying and analyzing mediation pathways.

State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.

The main function generateDataset() processes a user-supplied .R file that contains metadata parameters in order to generate actual data. The metadata parameters have to be structured in the form of metadata objects, the format of which is outlined in the package vignette. This approach allows to generate artificial data in a transparent and reproducible manner.

Simulating the sequential multi-arm multi-stage or platform trial with Bayesian approach using the rstan package, which provides the R interface for the Stan. This package supports fixed ratio and Bayesian adaptive randomization approaches for randomization. Additionally, it allows for the study of time trend problems in platform trials. There are demos available for a multi-arm multi-stage trial with two different null scenarios, as well as for Bayesian trial cutoff screening. The Bayesian adaptive randomisation approaches are described in: Trippa et al. (2012) <doi:10.1200/JCO.2011.39.8420> and Wathen et al. (2017) <doi:10.1177/1740774517692302>. The randomisation algorithm is described in: Zhao W <doi:10.1016/j.cct.2015.06.008>. The analysis methods of time trend effect in platform trial are described in: Saville et al. (2022) <doi:10.1177/17407745221112013> and Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>.

Approximate Euclidean k-nearest neighbour search routines that operate on bigmemory::big.matrix data through Annoy indexes created with RcppAnnoy'. The package builds persistent on-disk indexes plus sidecar metadata from streamed big.matrix rows, supports euclidean, angular, Manhattan, and dot-product Annoy metrics, and can either return in-memory results or stream neighbour indices and distances into destination bigmemory matrices. Explicit index life cycle helpers, stronger metadata validation, descriptor-aware file-backed workflows, and benchmark helpers are also included.

R client to the Binance Public Rest API for data collection on cryptocurrencies, portfolio management and trading: <https://github.com/binance/binance-spot-api-docs/blob/master/rest-api.md>.

Fork-safe, raw access to the Amazon Web Services ('AWS') SDK via the boto3 Python module, and convenient helper functions to query the Simple Storage Service ('S3') and Key Management Service ('KMS'), partial support for IAM', the Systems Manager Parameter Store and Secrets Manager'.

This package provides a unified syntax for the simulation-based comparison of different single-stage basket trial designs with a binary endpoint and equal sample sizes in all baskets. Methods include the designs by Baumann et al. (2025) <doi:10.1080/19466315.2024.2402275>, Schmitt and Baumann (2025) <doi:10.1080/19466315.2025.2486231>, Fujikawa et al. (2020) <doi:10.1002/bimj.201800404>, Berry et al. (2020) <doi:10.1177/1740774513497539>, and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>. For the latter two designs, the functions are mostly wrappers for functions provided by the package bhmbasket'.

This package provides a collection of integrated tools designed to seamlessly interact with each other for the analysis of biogenic silica bSi in inland and marine sediments. These tools share common data representations and follow a consistent API design. The primary goal of the bSi package is to simplify the installation process, facilitate data loading, and enable the analysis of multiple samples for biogenic silica fluxes. This package is designed to enhance the efficiency and coherence of the entire bSi analytic workflow, from data loading to model construction and visualization tailored towards reconstructing productivity in aquatic ecosystems.

An implementation of the bridge distribution with logit-link in R. In Wang and Louis (2003) <DOI:10.1093/biomet/90.4.765>, such a univariate bridge distribution was derived as the distribution of the random intercept that bridged a marginal logistic regression and a conditional logistic regression. The conditional and marginal regression coefficients are a scalar multiple of each other. Such is not the case if the random intercept distribution was Gaussian.

Bayesian approach to multidimensional scaling. The package consists of implementations of the methods of Oh and Raftery (2001) <doi:10.1198/016214501753208690>.

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.

Implementation of the record linkage methodology proposed by Sadinle (2017) <doi:10.1080/01621459.2016.1148612>. It handles the bipartite record linkage problem, where two duplicate-free datafiles are to be merged.

This package provides functions to compute the asymptotic covariance matrices of mixing and unmixing matrix estimates of the following blind source separation (BSS) methods: symmetric and squared symmetric FastICA, regular and adaptive deflation-based FastICA, FOBI, JADE, AMUSE and deflation-based and symmetric SOBI. Also functions to estimate these covariances based on data are available.

Working with reproducible reports or any other similar projects often require to run the script that builds the output file in a specified way. buildr can help you organize, modify and comfortably run those scripts. The package provides a set of functions that interactively guides you through the process and that are available as RStudio Addin, meaning you can set up the keyboard shortcuts, enabling you to choose and run the desired build script with one keystroke anywhere anytime.

This package provides functions to visualize combined action data in ggplot2'. Also provides functions for producing full BRAID analysis reports with custom layouts and aesthetics, using the BRAID method originally described in Twarog et al. (2016) <doi:10.1038/srep25523>.

Calculate the operating characteristics of the Bayesian Optimal Interval with Back Filling Design for dose escalation in early-phase oncology trials.

Distributes Gaussian process calculations across nodes in a distributed memory setting, using Rmpi. The bigGP class provides high-level methods for maximum likelihood with normal data, prediction, calculation of uncertainty (i.e., posterior covariance calculations), and simulation of realizations. In addition, bigGP provides an API for basic matrix calculations with distributed covariance matrices, including Cholesky decomposition, back/forwardsolve, crossproduct, and matrix multiplication.

This package provides a registry of APIs listed on <https://bund.dev> and a core OpenAPI client layer to explore specs and perform requests. Adapter helpers return tidy data frames for supported APIs, with optional response caching and rate limiting guidance.

This package provides functions to download and tidy statistical data published by the Bank of England <https://www.bankofengland.co.uk>. Covers Bank Rate, SONIA', gilt yields, exchange rates, mortgage rates, mortgage approvals, consumer credit, and money supply. Series are fetched from the Bank of England Interactive Statistical Database using its CSV endpoint. Data is cached locally between sessions.

It brings together several aspects of biodiversity data-cleaning in one place. bdc is organized in thematic modules related to different biodiversity dimensions, including 1) Merge datasets: standardization and integration of different datasets; 2) pre-filter: flagging and removal of invalid or non-interpretable information, followed by data amendments; 3) taxonomy: cleaning, parsing, and harmonization of scientific names from several taxonomic groups against taxonomic databases locally stored through the application of exact and partial matching algorithms; 4) space: flagging of erroneous, suspect, and low-precision geographic coordinates; and 5) time: flagging and, whenever possible, correction of inconsistent collection date. In addition, it contains features to visualize, document, and report data quality â which is essential for making data quality assessment transparent and reproducible. The reference for the methodology is Ribeiro and colleagues (2022) <doi:10.1111/2041-210X.13868>.