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'.

This package provides a very simple syntax for the user to generate custom plot(s) without having to remember complicated ggplot2 syntax. The chartql package uses ggplot2 and manages all the syntax complexities internally. As an example, to generate a bar chart of company sales faceted by product category further faceted by season of the year, we simply write: "CHART bar X category, season Y sales".

Markov chain Monte Carlo based inference routines for collapsed latent position cluster models or social networks, which includes searches over the model space (number of clusters in the latent position cluster model). The label switching algorithm used is that of Nobile and Fearnside (2007) <doi:10.1007/s11222-006-9014-7> which relies on the algorithm of Carpaneto and Toth (1980) <doi:10.1145/355873.355883>.

This package provides tools for accessing and analyzing eBird Status and Trends Data Products (<https://science.ebird.org/en/status-and-trends>). eBird (<https://ebird.org/home>) is a global database of bird observations collected by member of the public. eBird Status and Trends uses these data to model global bird distributions, abundances, and population trends at a high spatial and temporal resolution.

Fully robust versions of the elastic net estimator are introduced for linear and binary and multinomial regression, in particular high dimensional data. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied. A reweighting step is added to improve the statistical efficiency of the proposed estimators. Selecting appropriate tuning parameters for elastic net penalties are done via cross-validation.

The EpiSimR package provides an interactive shiny app based on deterministic compartmental mathematical modeling for simulating and visualizing the dynamics of epidemic and endemic disease spread. It allows users to explore various intervention strategies, including vaccination and isolation, by adjusting key epidemiological parameters. The methodology follows the approach described by Brauer (2008) <doi:10.1007/978-3-540-78911-6_2>. Thanks to shiny package.

Point and interval estimation in dual frame surveys. In contrast to classic sampling theory, where only one sampling frame is considered, dual frame methodology assumes that there are two frames available for sampling and that, overall, they cover the entire target population. Then, two probability samples (one from each frame) are drawn and information collected is suitably combined to get estimators of the parameter of interest.

This package provides a framework for predicting retention times in liquid chromatography. Users can train custom models for specific chromatography columns, predict retention times using existing models, or adjust existing models to account for altered experimental conditions. The provided functionalities can be accessed either via the R console or via a graphical user interface. Related work: Bonini et al. (2020) <doi:10.1021/acs.analchem.9b05765>.

This package provides a suite of function-building tools centered around a (forward) composition operator, %>>>%, which extends the semantics of the magrittr %>% operator and supports Tidyverse quasiquotation. It enables you to construct composite functions that can be inspected and transformed as list-like objects. In conjunction with %>>>%, a compact function constructor, fn(), and a partial-application constructor, partial(), are also provided; both support quasiquotation.

The American Community Survey (ACS) <https://www.census.gov/programs-surveys/acs> offers geodatabases with geographic information and associated data of interest to researchers in the area. The goal of this package is to generate objects that allow us to access and consult the information available in various formats, such as in GeoPackage format or in multidimensional ROLAP (Relational On-Line Analytical Processing) star format.

This package provides R functions to access the API of the project and repository management web application GitLab'. For many common tasks (repository file access, issue assignment and status, commenting) convenience wrappers are provided, and in addition the full API can be used by specifying request locations. GitLab is open-source software and can be self-hosted or used on <https://about.gitlab.com>.

This package provides functions and datasets to support Smilde, Marini, Westerhuis and Liland (2025, ISBN: 978-1-394-21121-0) "Analysis of Variance for High-Dimensional Data - Applications in Life, Food and Chemical Sciences". This implements and imports a collection of methods for HD-ANOVA data analysis with common interfaces, result- and plotting functions, multiple real data sets and four vignettes covering a range different applications.

Hadamard matrix based statistical designs are of immense importance as the resultant designs carry various desirable characterizing properties. Constructing Partially Balanced Incomplete Block Designs (PBIBds) using Kronecker product of incidence matrices of Balanced Incomplete Block (BIB) and Partially Balanced Incomplete Block (PBIB) designs is much evident from literature. Here, we have constructed Incomplete Block Designs (IBDs) based on Hadamard matrices and Kronecker product of Hadamard matrices.

This package provides functions to facilitate inverse estimation (e.g., calibration) in linear, generalized linear, nonlinear, and (linear) mixed-effects models. A generic function is also provided for plotting fitted regression models with or without confidence/prediction bands that may be of use to the general user. For a general overview of these methods, see Greenwell and Schubert Kabban (2014) <doi:10.32614/RJ-2014-009>.

Time series plain text conversion and data visualization. It allows to transform IDEAM (Instituto de Hidrologia, Meteorologia y Estudios Ambientales) daily series from plain text to CSV files or data frames in R. Additionally, it is possible to obtain exploratory graphs from times series. IDEAMâ s data is freely delivered under formal request through the official web page <http://www.ideam.gov.co/solicitud-de-informacion>.

Decomposition of income inequality by groups formed of individuals possessing similar characteristics (e.g., sex, education, age) and their income sources at the same time. Decomposition of the Theil index is based on Giammatteo, M. (2007) <https://www.lisdatacenter.org/wps/liswps/466.pdf>. Decomposition of the squared coefficient of variation is based on Garcia-Penalosa, C., & Orgiazzi, E. (2013) <doi:10.1111/roiw.12054>.

This package implements the kernel method of test equating as defined in von Davier, A. A., Holland, P. W. and Thayer, D. T. (2004) <doi:10.1007/b97446> and Andersson, B. and Wiberg, M. (2017) <doi:10.1007/s11336-016-9528-7> using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting Gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.

An easy-to-use ndjson (newline-delimited JSON') logger. It provides a set of wrappers for base R's message(), warning(), and stop() functions that maintain identical functionality, but also log the handler message to an ndjson log file. No change in existing code is necessary to use this package, and only a few additional adjustments are needed to fully utilize its potential.

This package provides a nonparametric method to approximate Laplacian graph spectra of a network with ordered vertices. This provides a computationally efficient algorithm for obtaining an accurate and smooth estimate of the graph Laplacian basis. The approximation results can then be used for tasks like change point detection, k-sample testing, and so on. The primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).

Computes martingale difference correlation (MDC), martingale difference divergence, and their partial extensions to assess conditional mean dependence. The methods are based on Shao and Zhang (2014) <doi:10.1080/01621459.2014.887012>. Additionally, introduces a novel hypothesis test for evaluating covariate effects on the cure rate in mixture cure models, using MDC-based statistics. The methodology is described in Monroy-Castillo et al. (2025, manuscript submitted).

Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.

Provide principally an eponymic function that numerically computes the Le Cam's one-step estimator for an independent and identically distributed sample. One-step estimation is asymptotically efficient (see L. Le Cam (1956) <https://projecteuclid.org/euclid.bsmsp/1200501652>) and can be computed faster than the maximum likelihood estimator for large observation samples, see e.g. Brouste et al. (2021) <doi:10.32614/RJ-2021-044>.

The purpose of PH1XBAR is to build a Phase I Shewhart control chart for the basic Shewhart, the variance components and the ARMA models in R for subgrouped and individual data. More details can be found: Yao and Chakraborti (2020) <doi: 10.1002/qre.2793>, Yao and Chakraborti (2021) <doi: 10.1080/08982112.2021.1878220>, and Yao et al. (2023) <doi: 10.1080/00224065.2022.2139783>.

Calculates profile repeatability for replicate stress response curves, or similar time-series data. Profile repeatability is an individual repeatability metric that uses the variances at each timepoint, the maximum variance, the number of crossings (lines that cross over each other), and the number of replicates to compute the repeatability score. For more information see Reed et al. (2019) <doi:10.1016/j.ygcen.2018.09.015>.