bc is an arbitrary precision numeric processing language. It includes an interactive environment for evaluating mathematical statements. Its syntax is similar to that of C, so basic usage is familiar. It also includes "dc", a reverse-polish calculator.

BCC is a toolkit for creating efficient kernel tracing and manipulation programs, and includes several useful tools and examples. It makes use of extended BPF (Berkeley Packet Filters), formally known as eBPF, a new feature that was first added to Linux 3.15. Much of what BCC uses requires Linux 4.1 and above.

GrblHAL CNC command sender, autoleveler, g-code editor, digitizer, CAM and swiss army knife for all your CNC needs. The sender is robust and fast able to work nicely with old or slow hardware.

Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020) <doi:10.1214/19-BA1195> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <arXiv:1309.1906>.

Implementation of bivariate binomial, geometric, and Poisson distributions based on conditional specifications. The package also includes tools for data generation and goodness-of-fit testing for these three distribution families. For methodological details, see Ghosh, Marques, and Chakraborty (2025) <doi:10.1080/03610926.2024.2315294>, Ghosh, Marques, and Chakraborty (2023) <doi:10.1080/03610918.2021.2004419>, and Ghosh, Marques, and Chakraborty (2021) <doi:10.1080/02664763.2020.1793307>.

This package provides methods for choosing the rank of an SVD (singular value decomposition) approximation via cross validation. The package provides both Gabriel-style "block" holdouts and Wold-style "speckled" holdouts. It also includes an implementation of the SVDImpute algorithm. For more information about Bi-cross-validation, see Owen & Perry's 2009 AoAS article (at <arXiv:0908.2062>) and Perry's 2009 PhD thesis (at <arXiv:0909.3052>).

Applies Beta Control Charts to defined values. The Beta Chart presents control limits based on the Beta probability distribution, making it suitable for monitoring fraction data from a Binomial distribution as a replacement for p-Charts. The Beta Chart has been applied in three real studies and compared with control limits from three different schemes. The comparative analysis showed that: (i) the Beta approximation to the Binomial distribution is more appropriate for values confined within the [0, 1] interval; and (ii) the proposed charts are more sensitive to the average run length (ARL) in both in-control and out-of-control process monitoring. Overall, the Beta Charts outperform the Shewhart control charts in monitoring fraction data. For more details, see à ngelo Márcio Oliveira Santâ Anna and Carla Schwengber ten Caten (2012) <doi:10.1016/j.eswa.2012.02.146>.

An implementation of a collection of tools for exact Bayesian inference with discrete times series. This package contains functions that can be used for prediction, model selection, estimation, segmentation/change-point detection and other statistical tasks. Specifically, the functions provided can be used for the exact computation of the prior predictive likelihood of the data, for the identification of the a posteriori most likely (MAP) variable-memory Markov models, for calculating the exact posterior probabilities and the AIC and BIC scores of these models, for prediction with respect to log-loss and 0-1 loss and segmentation/change-point detection. Example data sets from finance, genetics, animal communication and meteorology are also provided. Detailed descriptions of the underlying theory and algorithms can be found in [Kontoyiannis et al. Bayesian Context Trees: Modelling and exact inference for discrete time series. Journal of the Royal Statistical Society: Series B (Statistical Methodology), April 2022. Available at: <arXiv:2007.14900> [stat.ME], July 2020] and [Lungu et al. Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees <arXiv:2203.04341> [stat.ME], March 2022].

cdrecord converts a CD image in a ".bin / .cue" format (sometimes ".raw / .cue") to a set of .iso and .cdr tracks.

Search and access more than ten thousand datasets included in BCRPDATA (see <https://estadisticas.bcrp.gob.pe/estadisticas/series/ayuda/bcrpdata> for more information).

BCUnit is a fork of the defunct project CUnit, with several fixes and patches applied. It is a unit testing framework for writing, administering, and running unit tests in C.

BcG729 is an implementation of both encoder and decoder of the ITU G729 speech codec. The library written in C 99 is fully portable and can be executed on many platforms including both ARM and x86 processors. It supports concurrent channels encoding and decoding for multi call application such as conferencing.

This package provides a Bayesian model averaging approach to causal effect estimation based on the BCEE algorithm. Currently supports binary or continuous exposures and outcomes. For more details, see Talbot et al. (2015) <doi:10.1515/jci-2014-0035> Talbot and Beaudoin (2022) <doi:10.1515/jci-2021-0023>.

This package implements a wide variety of one- and two-parameter Bayesian CRM designs. The program can run interactively, allowing the user to enter outcomes after each cohort has been recruited, or via simulation to assess operating characteristics. See Sweeting et al. (2013): <doi:10.18637/jss.v054.i13>.

This package provides functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals. Gail MH, Brinton LA, et al (1989) <doi:10.1093/jnci/81.24.1879>. Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.

This package produces an economic evaluation of a sample of suitable variables of cost and effectiveness / utility for two or more interventions, e.g. from a Bayesian model in the form of MCMC simulations. This package computes the most cost-effective alternative and produces graphical summaries and probabilistic sensitivity analysis, see Baio et al (2017) <doi:10.1007/978-3-319-55718-2>.

Developed for the following tasks. Simulating, computing maximum likelihood estimator, computing the Fisher information matrix, computing goodness-of-fit measures, and correcting bias of the ML estimator for a wide range of distributions fitted to units placed on progressive type-I interval censoring and progressive type-II censoring plans. The methods of Cox and Snell (1968) <doi:10.1111/j.2517-6161.1968.tb00724.x> and bootstrap method for computing the bias-corrected maximum likelihood estimator.

Users can estimate the treatment effect for multiple subgroups basket trials based on the Bayesian Cluster Hierarchical Model (BCHM). In this model, a Bayesian non-parametric method is applied to dynamically calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the Bayesian non-parametric clustering and the similarities between subgroups. To use this package, JAGS software and rjags package are required, and users need to pre-install them.

The Behavioral Change Point Analysis (BCPA) is a method of identifying hidden shifts in the underlying parameters of a time series, developed specifically to be applied to animal movement data which is irregularly sampled. The method is based on: E. Gurarie, R. Andrews and K. Laidre A novel method for identifying behavioural changes in animal movement data (2009) Ecology Letters 12:5 395-408. A development version is on <https://github.com/EliGurarie/bcpa>. NOTE: the BCPA method may be useful for any univariate, irregularly sampled Gaussian time-series, but animal movement analysts are encouraged to apply correlated velocity change point analysis as implemented in the smoove package, as of this writing on GitHub at <https://github.com/EliGurarie/smoove>. An example of a univariate analysis is provided in the UnivariateBCPA vignette.

This package provides a C library for Broadcom BCM 2835 as used in the Raspberry Pi

B.Choppr cuts the audio input stream into a repeated sequence of up to 16 chops. Each chop can be leveled up or down (gating). B.Choppr is the successor of B.Slizr.

This package provides bias-corrected estimates for the regression coefficients of a marginal model estimated with generalized estimating equations. Details about the bias formula used are in Lunardon, N., Scharfstein, D. (2017) <doi:10.1002/sim.7366>.

This package provides a collection of functions for structure learning of causal networks and estimation of joint causal effects from observational Gaussian data. Main algorithm consists of a Markov chain Monte Carlo scheme for posterior inference of causal structures, parameters and causal effects between variables. References: F. Castelletti and A. Mascaro (2021) <doi:10.1007/s10260-021-00579-1>, F. Castelletti and A. Mascaro (2022) <doi:10.48550/arXiv.2201.12003>.

This Rcpp-based package implements a highly efficient data structure and algorithm for performing alignment of short reads from CRISPR or shRNA screens to reference barcode library. Sequencing error are considered and matching qualities are evaluated based on Phred scores. A Bayes classifier is employed to predict the originating barcode of a read. The package supports provision of user-defined probability models for evaluating matching qualities. The package also supports multi-threading.