Easy data analysis and quality checks which are commonly used in data science. It combines the tabular and graphical visualization for easier usability. This package also creates an R Notebook with detailed data exploration with one function call. The notebook can be made interactive.

Optimize one or two-arm, two-stage designs for clinical trials with respect to several implemented objective criteria or custom objectives. Optimization under uncertainty and conditional (given stage-one outcome) constraints are supported. See Pilz et al. (2019) <doi:10.1002/sim.8291> and Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09> for details.

This package provides a comprehensive toolkit for astronomical and cosmological computations. Provides functions for angular coordinate conversions (degrees, hours-minutes-seconds, degrees-minutes-seconds, and radians), access to fundamental physical constants, queries to the Gaia Archive TAP (Table Access Protocol) service, cosmological distance calculations, early-universe thermal physics including photon density, Saha equation solutions, and a full thermal-cosmology module covering the Hubble rate in the radiation-dominated era, effective relativistic degrees of freedom, entropy density, equilibrium yields, the Boltzmann relic-abundance ('pebble') equation for WIMP freeze-out, the freeze-out temperature solver, and the Peebles equation for hydrogen recombination. Also includes the Drake equation for estimating the number of communicating extraterrestrial civilisations in the Milky Way.

An interface to Azure Data Explorer', also known as Kusto', a fast, distributed data exploration service from Microsoft: <https://azure.microsoft.com/en-us/products/data-explorer/>. Includes DBI and dplyr interfaces, with the latter modelled after the dbplyr package, whereby queries are translated from R into the native KQL query language and executed lazily. On the admin side, the package extends the object framework provided by AzureRMR to support creation and deletion of databases, and management of database principals. Part of the AzureR family of packages.

This package provides an interface to the algorithm selection benchmark library at <https://www.coseal.net/aslib/> and the LLAMA package (<https://cran.r-project.org/package=llama>) for building algorithm selection models; see Bischl et al. (2016) <doi:10.1016/j.artint.2016.04.003>.

This package provides methods for assessing animal movement from telemetry and biologging data using non-parametric Bayesian methods. This includes features for pre- processing and analysis of data, as well as the visualization of results from the models. This framework does not rely on standard parametric density functions, which provides flexibility during model fitting. Further details regarding part of this framework can be found in Cullen et al. (2022) <doi:10.1111/2041-210X.13745>.

This is a port of the WTC MATLAB package written by Aslak Grinsted and the wavelet program written by Christopher Torrence and Gibert P. Compo. This package can be used to perform univariate and bivariate (cross-wavelet, wavelet coherence, wavelet clustering) analyses.

This package provides functions for blind source separation over multivariate spatial data, and useful statistics for evaluating performance of estimation on mixing matrix. BSSoverSpace is based on an eigen analysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and thus can handle moderately high-dimensional random fields. This package is an implementation of the method described in Zhang, Hao and Yao (2022)<arXiv:2201.02023>.

Regression for data too large to fit in memory. This package functions exactly like the biglm package, but works with later versions of R.

The function estimates the hazard function non parametrically from a survival object (possibly adjusted for covariates). The smoothed estimate is based on B-splines from the perspective of generalized linear mixed models. Left truncated and right censoring data are allowed. The package is based on the work in Rebora P (2014) <doi:10.32614/RJ-2014-028>.

Estimates the directed Kullback-Leibler divergence between two bivariate continuous distributions by numerical integration of kernel density estimates. Also computes pairwise divergences among groups and exact divergences for discrete, bivariate normal, bivariate Pareto type II, and independent bivariate Weibull models. The kernel estimator follows Chackochan, Sankaran and Nair (2026) <doi:10.1080/03610926.2025.2496687>.

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.

Implementation of the BRIk, FABRIk and FDEBRIk algorithms to initialise k-means. These methods are intended for the clustering of multivariate and functional data, respectively. They make use of the Modified Band Depth and bootstrap to identify appropriate initial seeds for k-means, which are proven to be better options than many techniques in the literature. Torrente and Romo (2021) <doi:10.1007/s00357-020-09372-3> It makes use of the functions kma and kma.similarity, from the archived package fdakma, by Alice Parodi et al.

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

This package implements the synthetic control method of Abadie, Diamond, and Hainmueller (2010) <doi:10.1198/jasa.2009.ap08746> within a Bayesian framework, enabling straightforward uncertainty quantification of treatment effects and other quantities of interest. Supports time-varying covariates with potentially time-varying effects, single or multiple treated units, and staggered treatment adoption. Provides methods for model assessment, comparison, and selection based on placebo studies, cross-validation, and posterior predictive checks. Posterior sampling is performed using Markov chain Monte Carlo via Stan.

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'.

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.

This package provides tools for sampling from the PolyaGamma distribution based on Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>. Useful for logistic regression.

From R 4.5.0, the datasets package includes the penguins and penguins_raw data sets popularised in the palmerpenguins package. basepenguins takes files that use the palmerpenguins package and converts them to work with the versions from datasets ('R >= 4.5.0). It does this by removing calls to library(palmerpenguins) and making the necessary changes to column names. Additionally, it provides helper functions to define new files paths for saving the output and a directory of example files to experiment with.

Model selection method with multiple block-wise imputation for block-wise missing data; see Xue, F., and Qu, A. (2021) <doi:10.1080/01621459.2020.1751176>.

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.

Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well as several types of bias correction for the least squares estimators of the autoregressive parameters as described in Zhou and Basawa (2005) <doi:10.1016/j.spl.2005.04.024> and Elbayoumi and Mostafa (2020) <doi:10.1002/sta4.342>. Currently, the bias correction methods supported include bootstrap (single, double and fast-double) bias correction and linear-bias-function-based bias correction. Functions for generating and plotting bifurcating autoregressive data from any BAR(p) model are also included. This new version includes calculating several type of bias-corrected and -uncorrected confidence intervals for the least squares estimators of the autoregressive parameters as described in Elbayoumi and Mostafa (2023) <doi:10.6339/23-JDS1092>.

Fits bifactor exploratory structural equation models (B-ESEM), together with standard exploratory structural equation modeling (ESEM) and confirmatory factor analysis (CFA), for continuous and ordinal data. Continuous models use lavaan native efa() blocks with robust maximum likelihood (MLR) estimation. Ordinal ESEM defaults to the lavaan weighted least squares mean- and variance-adjusted (WLSMV) estimator; ordinal B-ESEM uses a custom diagonally weighted least squares (DWLS) path with polychoric correlations from psych', rotation-delta standard errors via numDeriv', and a mean- and variance-adjusted chi-square. Target, geomin, and oblimin rotations use GPArotation'; the bifactor ESEM approach follows Morin, Arens and Marsh (2016) <doi:10.1080/10705511.2014.961800>. Additional features include multi-group measurement invariance (configural through strict, with partial invariance), ESEM-within-CFA conversion, McDonald's omega reliability suite, and the Mehrvarz and Rouder (2026) <doi:10.31234/osf.io/95enc_v3> alignment ratio check for independent cluster model confirmatory factor analysis (ICM-CFA) misspecification. An optional MplusAutomation interface allows side-by-side comparison with Mplus output.

Fit Bayesian models using brms'/'Stan with parsnip'/'tidymodels via bayesian <doi:10.5281/zenodo.4426836>. tidymodels is a collection of packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>). The technical details of brms and Stan are described in Bürkner (2017) <doi:10.18637/jss.v080.i01>, Bürkner (2018) <doi:10.32614/RJ-2018-017>, and Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.