Enables quick calibration of radiocarbon dates under various calibration curves (including user generated ones); age-depth modelling as per the algorithm of Haslett and Parnell (2008) <DOI:10.1111/j.1467-9876.2008.00623.x>; Relative sea level rate estimation incorporating time uncertainty in polynomial regression models (Parnell and Gehrels 2015) <DOI:10.1002/9781118452547.ch32>; non-parametric phase modelling via Gaussian mixtures as a means to determine the activity of a site (and as an alternative to the Oxcal function SUM(); currently unpublished), and reverse calibration of dates from calibrated into 14C years (also unpublished).

Computational tools for outlier detection and influence diagnostics in meta-analysis (Noma et al. (2025) <doi:10.1101/2025.09.18.25336125>). Bootstrap distributions of influence statistics are computed, and explicit thresholds for identifying outliers are provided. These methods can also be applied to the analysis of influential centers or regions in multicenter or multiregional clinical trials (Aoki, Noma and Gosho (2021) <doi:10.1080/24709360.2021.1921944>, Nakamura and Noma (2021) <doi:10.5691/jjb.41.117>).

Generate the James Blinding Index, as described in James et al (1996) <https://pubmed.ncbi.nlm.nih.gov/8841652/> and the Bang Blinding Index, as described in Bang et al (2004) <https://pubmed.ncbi.nlm.nih.gov/15020033/>. These are measures to assess whether or not satisfactory blinding has been maintained in a randomized, controlled, clinical trial. These can be generated for trial subjects, research coordinators and principal investigators, based upon standardized questionnaires that have been administered, to assess whether or not they can correctly guess to which treatment arm (e.g. placebo or treatment) subjects were assigned at randomization.

Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies.

Several implementations of a novel Bayesian hierarchical statistical model of nucleotide recoding RNA-seq experiments (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) for analyzing and comparing NR-seq datasets (see Vock and Simon (2023) <doi:10.1261/rna.079451.122>). NR-seq is a powerful extension of RNA-seq that provides information about the kinetics of RNA metabolism (e.g., RNA degradation rate constants), which is notably lacking in standard RNA-seq data. The statistical model makes maximal use of these high-throughput datasets by sharing information across transcripts to significantly improve uncertainty quantification and increase statistical power. bakR includes a maximally efficient implementation of this model for conservative initial investigations of datasets. bakR also provides more highly powered implementations using the probabilistic programming language Stan to sample from the full posterior distribution. bakR performs multiple-test adjusted statistical inference with the output of these model implementations to help biologists separate signal from background. Methods to automatically visualize key results and detect batch effects are also provided.

An implementation of sensitivity and robustness methods in Bayesian networks in R. It includes methods to perform parameter variations via a variety of co-variation schemes, to compute sensitivity functions and to quantify the dissimilarity of two Bayesian networks via distances and divergences. It further includes diagnostic methods to assess the goodness of fit of a Bayesian networks to data, including global, node and parent-child monitors. Reference: M. Leonelli, R. Ramanathan, R.L. Wilkerson (2022) <doi:10.1016/j.knosys.2023.110882>.

This package implements methods for fitting logistic biplot models to multivariate binary data. The logistic biplot represents individuals as points and binary variables as directed vectors in a low-dimensional subspace; the orthogonal projection of each individual onto a variable vector approximates the expected probability that the corresponding characteristic is present. Available fitting methods include conjugate gradient algorithms, a coordinate descent Majorization-Minimization (MM) algorithm, and a block coordinate descent algorithm based on data projection that supports matrices with missing values and allows new individuals to be projected as supplementary rows without refitting the model. A cross-validation procedure is provided to select the number of latent dimensions k. References: Babativa-Marquez and Vicente-Villardon (2021) <doi:10.3390/math9162015>; Vicente-Villardon and Galindo (2006, ISBN:9780470973196).

Battery reduction is a method used in data reduction. It uses Gram-Schmidt orthogonal rotations to find out a subset of variables best representing the original set of variables.

This package provides maximum likelihood estimates of the performance parameters that drive a binomial distribution of observed errors, and takes full advantage of zero error observations. High performance communications systems typically have inherent noise sources and other performance limitations that need to be estimated. Measurements made at high signal to noise ratios typically result in zero errors due to limitation in available measurement time. Package includes theoretical performance functions for common modulation schemes (Proakis, "Digital Communications" (1995, <ISBN:0-07-051726-6>)), polarization shifted QPSK (Agrell & Karlsson (2009, <DOI:10.1109/JLT.2009.2029064>)), and utility functions to work with the performance functions.

Calculates the prices of European options based on the universal solution provided by Bakshi, Cao and Chen (1997) <doi:10.1111/j.1540-6261.1997.tb02749.x>. This solution considers stochastic volatility, stochastic interest and random jumps. Please cite their work if this package is used.

Reproducibly classifies occurrence records into biome classes using 31 published global terrestrial biome schemes compiled by Fischer and colleagues (2022) <doi:10.1111/geb.13574>, provided as harmonised raster layers at 10x10 km resolution globally. Includes functions to choose the most suitable biome scheme for a dataset by a data-driven ranking, to classify occurrence records, and to tabulate and visualise the result. Works with user-provided occurrences or a taxon name, in which case occurrences are downloaded from GBIF (<https://www.gbif.org>) and cleaned automatically.

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.

The Bayesian Markov renewal mixed models take sequentially observed categorical data with continuous duration times, being either state duration or inter-state duration. These models comprehensively analyze the stochastic dynamics of both state transitions and duration times under the influence of multiple exogenous factors and random individual effect. The default setting flexibly models the transition probabilities using Dirichlet mixtures and the duration times using gamma mixtures. It also provides the flexibility of modeling the categorical sequences using Bayesian Markov mixed models alone, either ignoring the duration times altogether or dividing duration time into multiples of an additional category in the sequence by a user-specific unit. The package allows extensive inference of the state transition probabilities and the duration times as well as relevant plots and graphs. It also includes a synthetic data set to demonstrate the desired format of input data set and the utility of various functions. Methods for Bayesian Markov renewal mixed models are as described in: Abhra Sarkar et al., (2018) <doi:10.1080/01621459.2018.1423986> and Yutong Wu et al., (2022) <doi:10.1093/biostatistics/kxac050>.

Approximates best-subset selection (L0) regression with an iteratively adaptive Ridge (L2) penalty for large-scale models. This package uses Cyclops for an efficient implementation and the iterative method is described in Kawaguchi et al (2020) <doi:10.1002/sim.8438> and Li et al (2021) <doi:10.1016/j.jspi.2020.12.001>.

Bayesian adaptive randomization is also called outcome adaptive randomization, which is increasingly used in clinical trials.

Bayesian quantile regression using the asymmetric Laplace distribution, both continuous as well as binary dependent variables are supported. The package consists of implementations of the methods of Yu & Moyeed (2001) <doi:10.1016/S0167-7152(01)00124-9>, Benoit & Van den Poel (2012) <doi:10.1002/jae.1216> and Al-Hamzawi, Yu & Benoit (2012) <doi:10.1177/1471082X1101200304>. To speed up the calculations, the Markov Chain Monte Carlo core of all algorithms is programmed in Fortran and called from R.

This package provides methods for piecewise smooth regression. A piecewise smooth signal is estimated by applying a bootstrapped test recursively (binary segmentation approach). Each bootstrapped test decides whether the underlying signal is smooth on the currently considered subsegment or contains at least one further change-point.

Computation of bootstrap confidence intervals in an almost automatic fashion as described in Efron and Narasimhan (2020, <doi:10.1080/10618600.2020.1714633>).

Implementing the Block Coordinate Ascent with One-Step Generalized Rosen (BCA1SG) algorithm on the semiparametric models for panel count data, interval-censored survival data, and degradation data. A comprehensive description of the BCA1SG algorithm can be found in Wang et al. (2020) <https://github.com/yudongstat/BCA1SG/blob/master/BCA1SG.pdf>. For details of the semiparametric models for panel count data, interval-censored survival data, and degradation data, please see Wellner and Zhang (2007) <doi:10.1214/009053607000000181>, Huang and Wellner (1997) <ISBN:978-0-387-94992-5>, and Wang and Xu (2010) <doi:10.1198/TECH.2009.08197>, respectively.

This package provides datasets and functions used for analysis and visualizations in the Bayes Rules! book (<https://www.bayesrulesbook.com>). The package contains a set of functions that summarize and plot Bayesian models from some conjugate families and another set of functions for evaluation of some Bayesian models.

Generates bivariate residual plots with simulation polygons for any diagnostics and bivariate model from which functions to extract the desired diagnostics, simulate new data and refit the models are available.

This package provides the functions for Brunner-Munzel test and permuted Brunner-Munzel test, which enable to use formula, matrix, and table as argument. These functions are based on Brunner and Munzel (2000) <doi:10.1002/(SICI)1521-4036(200001)42:1%3C17::AID-BIMJ17%3E3.0.CO;2-U> and Neubert and Brunner (2007) <doi:10.1016/j.csda.2006.05.024>, and are written with FORTRAN.

Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the CellCODE R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses.

Bayesian Nonparametric sensitivity analysis of multiple testing procedures for p values with arbitrary dependencies, based on the Dirichlet process prior distribution.