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.

Interactive box plot using plotly for clinical trial analysis.

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>.

Supervised learning using Boltzmann Bayes model inference, which extends naive Bayes model to include interactions. Enables classification of data into multiple response groups based on a large number of discrete predictors that can take factor values of heterogeneous levels. Either pseudo-likelihood or mean field inference can be used with L2 regularization, cross-validation, and prediction on new data. <doi:10.18637/jss.v101.i05>.

Decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. BFAST can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. The algorithm can be extended to label detected changes with information on the parameters of the fitted piecewise linear models. BFAST monitoring functionality is described in Verbesselt et al. (2010) <doi:10.1016/j.rse.2009.08.014>. BFAST monitor provides functionality to detect disturbance in near real-time based on BFAST'- type models, and is described in Verbesselt et al. (2012) <doi:10.1016/j.rse.2012.02.022>. BFAST Lite approach is a flexible approach that handles missing data without interpolation, and will be described in an upcoming paper. Furthermore, different models can now be used to fit the time series data and detect structural changes (breaks).

It provides access to and information about the most important Brazilian economic time series - from the Getulio Vargas Foundation <http://portal.fgv.br/en>, the Central Bank of Brazil <http://www.bcb.gov.br> and the Brazilian Institute of Geography and Statistics <http://www.ibge.gov.br>. It also presents tools for managing, analysing (e.g. generating dynamic reports with a complete analysis of a series) and exporting these time series.

An automated graphical exploratory data analysis (EDA) tool that introduces: a.) wideplot graphics for exploring the structure of a dataset through a grid of variables and graphic types. b.) longplot graphics, which present the entire catalog of available graphics for representing a particular variable using a grid of graphic types and variations on these types. c.) plotup function, which presents a particular graphic for a specific variable of a dataset. The plotup() function also makes it possible to obtain the code used to generate the graphic, meaning that the user can adjust its properties as needed. d.) matrixplot graphics that is a grid of a particular graphic showing bivariate relationships between all pairs of variables of a certain(s) type(s) in a multivariate data set.

This package provides comprehensive tools for blinded sample size re-estimation (BSSR) in two-arm clinical trials with binary endpoints. Unlike traditional fixed-sample designs, BSSR allows adaptive sample size adjustments during trials while maintaining statistical integrity and study blinding. Implements five exact statistical tests: Pearson chi-squared, Fisher exact, Fisher mid-p, Z-pooled exact unconditional, and Boschloo exact unconditional tests. Supports restricted, unrestricted, and weighted BSSR approaches with exact Type I error control. Statistical methods based on Mehrotra et al. (2003) <doi:10.1111/1541-0420.00051> and Kieser (2020) <doi:10.1007/978-3-030-49528-2_21>.

Building on the docking layout manager provided by dockViewR', this provides a flexible front-end to blockr.core'. It provides an extension mechanism which allows for providing means to manipulate a board object via panel-based user interface components.

This package provides methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored. We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results. We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.

This package provides a set of functions for doing analysis of A/B split test data and web metrics in general.

Computes Bayesian posterior distributions of predictions, marginal effects, and differences of marginal effects for various generalized linear models. Importantly, the posteriors are on the mean (response) scale, allowing for more natural interpretation than summaries on the link scale. Also, predictions and marginal effects of the count probabilities for Poisson and negative binomial models can be computed.

Deals with the braid groups. Includes creation of some specific braids, group operations, free reduction, and Bronfman polynomials. Braid theory has applications in fluid mechanics and quantum physics. The code is adapted from the Haskell library combinat', and is based on Birman and Brendle (2005) <doi:10.48550/arXiv.math/0409205>.

This package provides several methods for generating density functions based on binned data. Methods include step function, recursive subdivision, and optimized spline. Data are assumed to be nonnegative, the top bin is assumed to have no upper bound, but the bin widths need be equal. All PDF smoothing methods maintain the areas specified by the binned data. (Equivalently, all CDF smoothing methods interpolate the points specified by the binned data.) In practice, an estimate for the mean of the distribution should be supplied as an optional argument. Doing so greatly improves the reliability of statistics computed from the smoothed density functions. Includes methods for estimating the Gini coefficient, the Theil index, percentiles, and random deviates from a smoothed distribution. Among the three methods, the optimized spline (splinebins) is recommended for most purposes. The percentile and random-draw methods should be regarded as experimental, and these methods only support splinebins.

Fits boundary line models to datasets as proposed by Webb (1972) <doi:10.1080/00221589.1972.11514472> and makes statistical inferences about their parameters. Provides additional tools for testing datasets for evidence of boundary presence and selecting initial starting values for model optimization prior to fitting the boundary line models. It also includes tools for conducting post-hoc analyses such as predicting boundary values and identifying the most limiting factor (Miti, Milne, Giller, Lark (2024) <doi:10.1016/j.fcr.2024.109365>). This ensures a comprehensive analysis for datasets that exhibit upper boundary structures.

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 a framework to infer causality on binary data using techniques in frequent pattern mining and estimation statistics. Given a set of individual vectors S=x where x(i) is a realization value of binary variable i, the framework infers empirical causal relations of binary variables i,j from S in a form of causal graph G=(V,E) where V is a set of nodes representing binary variables and there is an edge from i to j in E if the variable i causes j. The framework determines dependency among variables as well as analyzing confounding factors before deciding whether i causes j. The publication of this package is at Chainarong Amornbunchornvej, Navaporn Surasvadi, Anon Plangprasopchok, and Suttipong Thajchayapong (2023) <doi:10.1016/j.heliyon.2023.e15947>.

Analysis of gene expression RNA-seq data using Bartlett-Adjusted Likelihood-based LInear model (BALLI). Based on likelihood ratio test, it provides comparisons for effect of one or more variables. See Kyungtaek Park (2018) <doi:10.1101/344929> for more information.

This package creates an interactive graphics interface to visualize backtest results of different financial instruments, such as equities, futures, and credit default swaps. The package does not run backtests on the given data set but displays a graphical explanation of the backtest results. Users can look at backtest graphics for different instruments, investment strategies, and portfolios. Summary statistics of different portfolio holdings are shown in the left panel, and interactive plots of profit and loss (P&L), net market value (NMV) and gross market value (GMV) are displayed in the right panel.

BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAST2 is commonly accompanied by BEAUti 2', Tracer and DensiTree'. babette provides for an alternative workflow of using all these tools separately. This allows doing complex Bayesian phylogenetics easily and reproducibly from R'.

Fast partial least squares (PLS) for dense and out-of-core data. Provides SIMPLS (straightforward implementation of a statistically inspired modification of the PLS method) and NIPALS (non-linear iterative partial least-squares) solvers, plus kernel-style PLS variants ('kernelpls and widekernelpls') with parity to pls'. Optimized for bigmemory'-backed matrices with streamed cross-products and chunked BLAS (Basic Linear Algebra Subprograms) (XtX/XtY and XXt/YX), optional file-backed score sinks, and deterministic testing helpers. Includes an auto-selection strategy that chooses between XtX SIMPLS, XXt (wide) SIMPLS, and NIPALS based on (n, p) and a configurable memory budget. About the package, Bertrand and Maumy (2023) <https://hal.science/hal-05352069>, and <https://hal.science/hal-05352061> highlighted fitting and cross-validating PLS regression models to big data. For more details about some of the techniques featured in the package, Dayal and MacGregor (1997) <doi:10.1002/(SICI)1099-128X(199701)11:1%3C73::AID-CEM435%3E3.0.CO;2-%23>, Rosipal & Trejo (2001) <https://www.jmlr.org/papers/v2/rosipal01a.html>, Tenenhaus, Viennet, and Saporta (2007) <doi:10.1016/j.csda.2007.01.004>, Rosipal (2004) <doi:10.1007/978-3-540-45167-9_17>, Rosipal (2019) <https://ieeexplore.ieee.org/document/8616346>, Song, Wang, and Bai (2024) <doi:10.1016/j.chemolab.2024.105238>. Includes kernel logistic PLS with C++'-accelerated alternating iteratively reweighted least squares (IRLS) updates, streamed reproducing kernel Hilbert space (RKHS) solvers with reusable centering statistics, and bootstrap diagnostics with graphical summaries for coefficients, scores, and cross-validation workflows, alongside dedicated plotting utilities for individuals, variables, ellipses, and biplots. The streaming backend uses far less memory and keeps memory bounded across data sizes. For PLS1, streaming is often fast enough while preserving a small memory footprint; for PLS2 it remains competitive with a bounded footprint. On small problems that fit comfortably in RAM (random-access memory), dense in-memory solvers are slightly faster; the crossover occurs as n or p grow and the Gram/cross-product cost dominates.

Boosting Regression Quantiles is a component-wise boosting algorithm, that embeds all boosting steps in the well-established framework of quantile regression. It is initialized with the corresponding quantile, uses a quantile-specific learning rate, and uses quantile regression as its base learner. The package implements this algorithm and allows cross-validation and stability selection.

This package implements the Butterworth-Induced Autoregressive ('BTWAR') model, where autoregressive coefficients are obtained from analog Butterworth filter prototypes mapped into the discrete-time domain using the Matched Z-Transform. The framework establishes a structured connection between frequency-domain filter design and time-domain autoregressive modeling. Model order selection is performed via nested rolling-origin cross-validation. Method described in Bras-Geraldes, Rocha and Martins (2026) <doi:10.3390/math14030479>.

This is a sub national population projection model for calculating population development. The model uses a cohort component method. Further reading: Stanley K. Smith: A Practitioner's Guide to State and Local Population Projections. 2013. <doi:10.1007/978-94-007-7551-0>.