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>.

This package provides Bayesian early-warning surveillance methods for monitoring healthcare performance and patient safety outcomes. The package draws on risk-adjusted monitoring frameworks developed by Steiner et al. (2000) <doi:10.1093/biostatistics/1.4.441>, Spiegelhalter et al. (2003) <doi:10.1002/sim.1546>, Cook et al. (2011) <doi:10.1136/bmjqs.2008.031831>, and Neuburger et al. (2017) <doi:10.1136/bmjqs-2016-005511>. The package implements Bayesian predictive modelling, risk-adjusted monitoring, early-warning signal detection, and graphical tools for continuous quality improvement and healthcare performance assessment.

This package implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 <doi:10.1016/j.spasta.2021.100496>; Orozco-Acosta et al., 2023 <doi:10.1016/j.cmpb.2023.107403> and Vicente et al., 2023 <doi:10.1007/s11222-023-10263-x>). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001).

This package provides a platform for interactive data analysis designed to simplify development, deployment, interaction, and exploration (TEDDIE). The package enables users to create customized analyses and deploy them to end users, who can perform interactive analyses and export results to RTF or HTML files. It allows developers to focus on R code for analysis rather than managing HTML or Shiny application code.

Bayesian kernel machine regression (from the bkmr package) is a Bayesian semi-parametric generalized linear model approach under identity and probit links. There are a number of functions in this package that extend Bayesian kernel machine regression fits to allow multiple-chain inference and diagnostics, which leverage functions from the future', rstan', and coda packages. Reference: Bobb, J. F., Henn, B. C., Valeri, L., & Coull, B. A. (2018). Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression. ; <doi:10.1186/s12940-018-0413-y>.

Bond Pricing and Fixed-Income Valuation of Selected Securities included here serve as a quick reference of Quantitative Methods for undergraduate courses on Fixed-Income and CFA Level I Readings on Fixed-Income Valuation, Risk and Return. CFA Institute ("CFA Program Curriculum 2020 Level I Volumes 1-6. (Vol. 5, pp. 107-151, pp. 237-299)", 2019, ISBN: 9781119593577). Barbara S. Petitt ("Fixed Income Analysis", 2019, ISBN: 9781119628132). Frank J. Fabozzi ("Handbook of Finance: Financial Markets and Instruments", 2008, ISBN: 9780470078143). Frank J. Fabozzi ("Fixed Income Analysis", 2007, ISBN: 9780470052211).

Bayesian optimal interval based on both efficacy and toxicity outcomes (BOIN-ET) design is a model-assisted oncology phase I/II trial design, aiming to establish an optimal biological dose accounting for efficacy and toxicity in the framework of dose-finding. Some extensions of BOIN-ET design are also available to allow for time-to-event efficacy and toxicity outcomes based on cumulative and pending data (time-to-event BOIN-ET: TITE-BOIN-ET), ordinal graded efficacy and toxicity outcomes (generalized BOIN-ET: gBOIN-ET), and their combination (TITE-gBOIN-ET). boinet is a package to implement the BOIN-ET design family and supports the conduct of simulation studies to assess operating characteristics of BOIN-ET, TITE-BOIN-ET, gBOIN-ET, and TITE-gBOIN-ET, where users can choose design parameters in flexible and straightforward ways depending on their own application.

This package implements a modified Newton-type algorithm (BSW algorithm) for solving the maximum likelihood estimation problem in fitting a log-binomial model under linear inequality constraints.

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.

Included here are babel routines for identifying unusual ribosome protected fragment counts given mRNA counts.

This package provides tools for pedigree quality control and genomic breed/line composition estimation in diploid and polyploid breeding populations. BIGpopA provides functions to check and correct common pedigree errors, assign parentage from SNP genotype data using Mendelian error rates, validate parent-offspring trios, and estimate genome-wide breed or line composition using quadratic programming. Supports both diploid and polyploid species. For more details about the included breedTools functions, see Funkhouser et al. (2017) <doi:10.2527/tas2016.0003>.

This package implements Bayesian model averaging for dynamic panels with weakly exogenous regressors as described in the paper by Moral-Benito (2013, <doi:10.1080/07350015.2013.818003>). The package provides functions to estimate dynamic panel data models and analyze the results of the estimation.

Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities. In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point. Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point. For further details, see: Alexander C. Murph et al. (2023) <doi:10.48550/arXiv.2310.02940>.

This package provides a lossless compressed data format that uses a combination of the LZ77 algorithm and Huffman coding <https://www.rfc-editor.org/rfc/rfc7932>. Brotli is similar in speed to deflate (gzip) but offers more dense compression.

Make some distributions from the C++ library Boost available in R'. In addition, the normal-inverse Gaussian distribution and the generalized inverse Gaussian distribution are provided. The distributions are represented by R6 classes. The method to sample from the generalized inverse Gaussian distribution is the one given in "Random variate generation for the generalized inverse Gaussian distribution" Luc Devroye (2012) <doi:10.1007/s11222-012-9367-z>.

Estimating the average causal effect based on the Bayesian Adjustment for Confounding (BAC) algorithm.

Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for proportional hazards models with piecewise constant hazard. The methodology and examples of applying the package are detailed in <doi:10.48550/arXiv.2404.05118>. The Bayesian clinical trial design methodology is described in Chen et al. (2011) <doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019) <doi:10.1093/biostatistics/kxy009>. The proportional hazards model with piecewise constant hazard is detailed in Ibrahim et al. (2001) <doi:10.1007/978-1-4757-3447-8>.

Perform fundamental analyses using Bayesian parametric and non-parametric inference (regression, anova, 1 and 2 sample inference, non-parametric tests, etc.). (Practically) no Markov chain Monte Carlo (MCMC) is used; all exact finite sample inference is completed via closed form solutions or else through posterior sampling automated to ensure precision in interval estimate bounds. Diagnostic plots for model assessment, and key inferential quantities (point and interval estimates, probability of direction, region of practical equivalence, and Bayes factors) and model visualizations are provided. Bayes factors are computed either by the Savage Dickey ratio given in Dickey (1971) <doi:10.1214/aoms/1177693507> or by Chib's method as given in xxx. Interpretations are from Kass and Raftery (1995) <doi:10.1080/01621459.1995.10476572>. ROPE bounds are based on discussions in Kruschke (2018) <doi:10.1177/2515245918771304>. Methods for determining the number of posterior samples required are described in Doss et al. (2014) <doi:10.1214/14-EJS957>. Bayesian model averaging is done in part by Feldkircher and Zeugner (2015) <doi:10.18637/jss.v068.i04>. Methods for contingency table analysis is described in Gunel et al. (1974) <doi:10.1093/biomet/61.3.545>. Variational Bayes (VB) methods are described in Salimans and Knowles (2013) <doi:10.1214/13-BA858>. Mediation analysis uses the framework described in Imai et al. (2010) <doi:10.1037/a0020761>. The loss-likelihood bootstrap used in the non-parametric regression modeling is described in Lyddon et al. (2019) <doi:10.1093/biomet/asz006>. Non-parametric survival methods are described in Qing et al. (2023) <doi:10.1002/pst.2256>. Methods used for the Bayesian Wilcoxon signed-rank analysis is given in Chechile (2018) <doi:10.1080/03610926.2017.1388402> and for the Bayesian Wilcoxon rank sum analysis in Chechile (2020) <doi:10.1080/03610926.2018.1549247>. Correlation analysis methods are carried out by Barch and Chechile (2023) <doi:10.32614/CRAN.package.DFBA>, and described in Lindley and Phillips (1976) <doi:10.1080/00031305.1976.10479154> and Chechile and Barch (2021) <doi:10.1016/j.jmp.2021.102638>. See also Chechile (2020, ISBN: 9780262044585).

Bayesian optimal design with futility and efficacy stopping boundaries (BOP2-FE) is a novel statistical framework for single-arm Phase II clinical trials. It enables early termination for efficacy when interim data are promising, while explicitly controlling Type I and Type II error rates. The design supports a variety of endpoint structures, including single binary endpoints, nested endpoints, co-primary endpoints, and joint monitoring of efficacy and toxicity. The package provides tools for enumerating stopping boundaries prior to trial initiation and for conducting simulation studies to evaluate the designâ s operating characteristics. Users can flexibly specify design parameters to suit their specific applications. For methodological details, refer to Xu et al. (2025) <doi:10.1080/10543406.2025.2558142>.

Imports benthic count data, reformats this data, and computes environmental inferences from this data.

Exact nearest-neighbour and radius-search routines that operate directly on bigmemory::big.matrix objects. The package streams row blocks through BLAS kernels, supports self-search and external-query search, exposes prepared references for repeated queries, and can build exact k-nearest-neighbour, radius, mutual k-nearest-neighbour, and shared-nearest-neighbour graphs. Version 0.3.0 adds execution plans, serializable prepared caches, resumable streamed graph jobs, coercion helpers, exact candidate reranking, and recall summaries for evaluating approximate neighbours.

Build and use B-splines for interpolation and regression. In case of regression, equality constraints as well as monotonicity and/or positivity of B-spline weights can be imposed. Moreover, knot positions can be on regular grid or be part of optimized parameters too (in addition to the spline weights). For this end, bspline is able to calculate Jacobian of basis vectors as function of knot positions. User is provided with functions calculating spline values at arbitrary points. These functions can be differentiated and integrated to obtain B-splines calculating derivatives/integrals at any point. B-splines of this package can simultaneously operate on a series of curves sharing the same set of knots. bspline is written with concern about computing performance that's why the basis and Jacobian calculation is implemented in C++. The rest is implemented in R but without notable impact on computing speed.

Easy-to-use, efficient, flexible and scalable tools for analyzing massive SNP arrays. Privé et al. (2018) <doi:10.1093/bioinformatics/bty185>.

This package performs a spatial Bayesian general linear model (GLM) for task functional magnetic resonance imaging (fMRI) data on the cortical surface. Additional models include group analysis and inference to detect thresholded areas of activation. Includes direct support for the CIFTI neuroimaging file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) <doi:10.1016/j.neuroimage.2022.118908>.