How to fit a straight line through a set of points with errors in both coordinates? The bfsl package implements the York regression (York, 2004 <doi:10.1119/1.1632486>). It provides unbiased estimates of the intercept, slope and standard errors for the best-fit straight line to independent points with (possibly correlated) normally distributed errors in both x and y. Other commonly used errors-in-variables methods, such as orthogonal distance regression, geometric mean regression or Deming regression are special cases of the bfsl solution.

R bindings for the various functions and statistical distributions provided by the Boost Math library <https://www.boost.org/doc/libs/latest/libs/math/doc/html/index.html>.

This package provides a system of functions and datasets to carry out quantitative analyses in the biological sciences. The package facilitates data management, exploratory analyses, and model assessment. Although it currently focuses on forest ecology, silviculture and decision-making, most of the package functions are applicable across several disciplines, including economics, environmental science, and healthcare.

This package provides functions and datasets for Jeff Gill: "Bayesian Methods: A Social and Behavioral Sciences Approach". First, Second, and Third Edition. Published by Chapman and Hall/CRC (2002, 2007, 2014) <doi:10.1201/b17888>.

Enables a user to consume the BambooHR API endpoints using R. The actual URL of the API will depend on your company domain, and will be handled by the package automatically once you setup the config file. The API documentation can be found here <https://documentation.bamboohr.com/docs>.

This package provides JAR to perform Markov chain Monte Carlo (MCMC) inference using the popular Bayesian Evolutionary Analysis by Sampling Trees BEAST X software library of Baele et al (2025) <doi:10.1038/s41592-025-02751-x>. BEAST X supports auto-tuning Metropolis-Hastings, slice, Hamiltonian Monte Carlo and Sequential Monte Carlo sampling for a large variety of composable standard and phylogenetic statistical models using high performance computing. By placing the BEAST X JAR in this package, we offer an efficient distribution system for BEAST X use by other R packages using CRAN.

Building on the docking layout manager provided by blockr.dock', this provides an extension that allows for visualizing and manipulating a blockr board using a DAG-based user interface powered by the g6R graph visualisation HTML widget.

An implementation of the model in Steorts (2015) <DOI:10.1214/15-BA965SI>, which performs Bayesian entity resolution for categorical and text data, for any distance function defined by the user. In addition, the precision and recall are in the package to allow one to compare to any other comparable method such as logistic regression, Bayesian additive regression trees (BART), or random forests. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.

This package provides a system to build, visualise and evaluate Bayesian belief networks. The methods are described in Stafford et al. (2015) <doi:10.12688/f1000research.5981.1>.

This package provides functions to create side-by-side boxplots for a continuous variable grouped by a two-level categorical variable, check normality assumptions using the Shapiro-Wilk test (Shapiro and Wilk (1965) <doi:10.2307/2333709>), and perform appropriate statistical tests such as the independent two-sample t-test (Student (1908) <doi:10.1093/biomet/6.1.1>) or the Mannâ Whitney U test ( Mannâ Whitney (1947) <doi:10.1214/aoms/1177730491>). Returns a publication-ready plot and test statistics including test statistic, degrees of freedom, and p-value.

Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis. This implements many models from Meager (2019) <doi:10.1257/app.20170299>.

Build decision trees and random forests for classification and regression. The implementation strikes a balance between minimizing computing efforts and maximizing the expected predictive accuracy, thus scales well to large data sets. Multi-threading is available through OpenMP <https://gcc.gnu.org/wiki/openmp>.

This package provides a class of Bayesian beta regression models for the analysis of continuous data with support restricted to an unknown finite support. The response variable is modeled using a four-parameter beta distribution with the mean or mode parameter depending linearly on covariates through a link function. When the response support is known to be (0,1), the above class of models reduce to traditional (0,1) supported beta regression models. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou and Huang (2022) <doi:10.1016/j.csda.2021.107345>.

This package provides a collection of S4 classes, methods and functions to create and visualize business plans. Different types of cash flows can be defined, which can then be used and tabulated to create profit and loss statements, cash flow plans, investment and depreciation schedules, loan amortization schedules, etc. The methods are designed to produce handsome tables in both PDF and HTML using RMarkdown or Shiny'.

Companion package, functions, data sets, examples for the book Patrice Bertail and Anna Dudek (2025), Bootstrap for Dependent Data, with an R package (by Bernard Desgraupes and Karolina Marek) - submitted. Kreiss, J.-P. and Paparoditis, E. (2003) <doi:10.1214/aos/1074290332> Politis, D.N., and White, H. (2004) <doi:10.1081/ETC-120028836> Patton, A., Politis, D.N., and White, H. (2009) <doi:10.1080/07474930802459016> Tsybakov, A. B. (2018) <doi:10.1007/b13794> Bickel, P., and Sakov, A. (2008) <doi:10.1214/18-AOS1803> Götze, F. and RaÄ kauskas, A. (2001) <doi:10.1214/lnms/1215090074> Politis, D. N., Romano, J. P., & Wolf, M. (1999, ISBN:978-0-387-98854-2) Carlstein E. (1986) <doi:10.1214/aos/1176350057> Künsch, H. (1989) <doi:10.1214/aos/1176347265> Liu, R. and Singh, K. (1992) <https://www.stat.purdue.edu/docs/research/tech-reports/1991/tr91-07.pdf> Politis, D.N. and Romano, J.P. (1994) <doi:10.1080/01621459.1994.10476870> Politis, D.N. and Romano, J.P. (1992) <https://www.stat.purdue.edu/docs/research/tech-reports/1991/tr91-07.pdf> Patrice Bertail, Anna E. Dudek. (2022) <doi:10.3150/23-BEJ1683> Dudek, A.E., LeÅ kow, J., Paparoditis, E. and Politis, D. (2014a) <https://ideas.repec.org/a/bla/jtsera/v35y2014i2p89-114.html> Beran, R. (1997) <doi:10.1023/A:1003114420352> B. Efron, and Tibshirani, R. (1993, ISBN:9780429246593) Bickel, P. J., Götze, F. and van Zwet, W. R. (1997) <doi:10.1007/978-1-4614-1314-1_17> A. C. Davison, D. Hinkley (1997) <doi:10.2307/1271471> Falk, M., & Reiss, R. D. (1989) <doi:10.1007/BF00354758> Lahiri, S. N. (2003) <doi:10.1007/978-1-4757-3803-2> Shimizu, K. .(2017) <doi:10.1007/978-3-8348-9778-7> Park, J.Y. (2003) <doi:10.1111/1468-0262.00471> Kirch, C. and Politis, D. N. (2011) <doi:10.48550/arXiv.1211.4732> Bertail, P. and Dudek, A.E. (2024) <doi:10.3150/23-BEJ1683> Dudek, A. E. (2015) <doi:10.1007/s00184-014-0505-9> Dudek, A. E. (2018) <doi:10.1080/10485252.2017.1404060> Bertail, P., Clémençon, S. (2006a) <https://ideas.repec.org/p/crs/wpaper/2004-47.html> Bertail, P. and Clémençon, S. (2006, ISBN:978-0-387-36062-1) RaduloviÄ , D. (2006) <doi:10.1007/BF02603005> Bertail, P. Politis, D. N. Rhomari, N. (2000) <doi:10.1080/02331880008802701> Nordman, D.J. Lahiri, S.N.(2004) <doi:10.1214/009053604000000779> Politis, D.N. Romano, J.P. (1993) <doi:10.1006/jmva.1993.1085> Hurvich, C. M. and Zeger, S. L. (1987, ISBN:978-1-4612-0099-4) Bertail, P. and Dudek, A. (2021) <doi:10.1214/20-EJS1787> Bertail, P., Clémençon, S. and Tressou, J. (2015) <doi:10.1111/jtsa.12105> Asmussen, S. (1987) <doi:10.1007/978-3-662-11657-9> Efron, B. (1979) <doi:10.1214/aos/1176344552> Gray, H., Schucany, W. and Watkins, T. (1972) <doi:10.2307/2335521> Quenouille, M.H. (1949) <doi:10.1111/j.2517-6161.1949.tb00023.x> Quenouille, M. H. (1956) <doi:10.2307/2332914> Prakasa Rao, B. L. S. and Kulperger, R. J. (1989) <https://www.jstor.org/stable/25050735> Rajarshi, M.B. (1990) <doi:10.1007/BF00050835> Dudek, A.E. Maiz, S. and Elbadaoui, M. (2014) <doi:10.1016/j.sigpro.2014.04.022> Beran R. (1986) <doi:10.1214/aos/1176349847> Maritz, J. S. and Jarrett, R. G. (1978) <doi:10.2307/2286545> Bertail, P., Politis, D., Romano, J. (1999) <doi:10.2307/2670177> Bertail, P. and Clémençon, S. (2006b) <doi:10.1007/0-387-36062-X_1> RaduloviÄ , D. (2004) <doi:10.1007/BF02603005> Hurd, H.L., Miamee, A.G. (2007) <doi:10.1002/9780470182833> Bühlmann, P. (1997) <doi:10.2307/3318584> Choi, E., Hall, P. (2000) <doi:10.1111/1467-9868.00244> Efron, B., Tibshirani, R. (1993, ISBN:9780429246593) Bertail, P., Clémençon, S. and Tressou, J. (2009) <doi:10.1007/s10687-009-0081-y> Bertail, P., Medina-Garay, A., De Lima-Medina, F. and Jales, I. (2024) <doi:10.1080/02331888.2024.2344670>.

Data about the bakers, challenges, and ratings for "The Great British Bake Off", from Wikipedia <https://en.wikipedia.org/wiki/The_Great_British_Bake_Off>.

This package implements two algorithms of detecting Bull and Bear markets in stock prices: the algorithm of Pagan and Sossounov (2002, <doi:10.1002/jae.664>) and the algorithm of Lunde and Timmermann (2004, <doi:10.1198/073500104000000136>). The package also contains functions for printing out the dating of the Bull and Bear states of the market, the descriptive statistics of the states, and functions for plotting the results. For the sake of convenience, the package includes the monthly and daily data on the prices (not adjusted for dividends) of the S&P 500 stock market index.

This package provides tools to model and forecast multivariate time series including Bayesian Vector heterogeneous autoregressive (VHAR) model by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>). bvhar can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.

The bias-corrected estimation methods for the receiver operating characteristics ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR) assumption.

This package provides tools for optimal and approximate state estimation as well as network inference of Partially-Observed Boolean Dynamical Systems.

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>.

This package provides a tuneable and interpretable method for relaxing the instrumental variables (IV) assumptions to infer treatment effects in the presence of unobserved confounding. For a treatment-associated covariate to be a valid IV, it must be (a) unconfounded with the outcome and (b) have a causal effect on the outcome that is exclusively mediated by the exposure. There is no general test of the validity of these IV assumptions for any particular pre-treatment covariate. However, if different pre-treatment covariates give differing causal effect estimates when treated as IVs, then we know at least some of the covariates violate these assumptions. budgetIVr exploits this fact by taking as input a minimum budget of pre-treatment covariates assumed to be valid IVs and idenfiying the set of causal effects that are consistent with the user's data and budget assumption. The following generalizations of this principle can be used in this package: (1) a vector of multiple budgets can be assigned alongside corresponding thresholds that model degrees of IV invalidity; (2) budgets and thresholds can be chosen using specialist knowledge or varied in a principled sensitivity analysis; (3) treatment effects can be nonlinear and/or depend on multiple exposures (at a computational cost). The methods in this package require only summary statistics. Confidence sets are constructed under the "no measurement error" (NOME) assumption from the Mendelian randomization literature. For further methodological details, please refer to Penn et al. (2024) <doi:10.48550/arXiv.2411.06913>.

Semi-supervised and unsupervised Bayesian mixture models that simultaneously infer the cluster/class structure and a batch correction. Densities available are the multivariate normal and the multivariate t. The model sampler is implemented in C++. This package is aimed at analysis of low-dimensional data generated across several batches. See Coleman et al. (2022) <doi:10.1101/2022.01.14.476352> for details of the model.

Design and analysis of two-arm binomial clinical (phase II) trials using Bayes factors. Implements Bayes factors for point-null and directional hypotheses, predictive densities under different hypotheses, and power and sample size calibration with optional frequentist type-I error and power.