This package provides a set of tools for empirical analysis of diversity (a number and frequency of different types in a population) and similarity (a number and frequency of shared types in two populations) in biological or ecological systems.

Multiplex temporal community detection with customizable interlayer coupling. Runs Louvain or Leiden community detection on each network layer and constructs interlayer ties using Jaccard similarity, overlap coefficient, node-strength weighted variants, or direct node identity links, and also provides a two-stage snapshot-and-match tracker that aligns independently detected per-layer communities across time with the Hungarian assignment algorithm. Supports user-specified layer connectivity via the layer_links argument, enabling adjacent-only temporal coupling that avoids the long-range pooling problem in standard multislice approaches.

Decodes meshes and point cloud data encoded by the Draco mesh compression library from Google. Note that this is only designed for basic decoding and not intended as a full scale wrapping of the Draco library.

Various diffusion models to forecast new product growth. Currently the package contains Bass, Gompertz, Gamma/Shifted Gompertz and Weibull curves. See Meade and Islam (2006) <doi:10.1016/j.ijforecast.2006.01.005>.

This package performs sensitivity analysis for the sharp null, attributable effects, and weak nulls in matched studies with continuous exposures and binary or continuous outcomes as described in Zhang, Small, Heng (2024) <doi:10.48550/arXiv.2401.06909> and Zhang, Heng (2024) <doi:10.48550/arXiv.2409.12848>. Two of the functions require installation of the Gurobi optimizer. Please see <https://docs.gurobi.com/current/#refman/ins_the_r_package.html> for guidance.

This package provides a comprehensive approach for identifying and estimating change points in multivariate time series through various statistical methods. Implements the multiple change point detection methodology from Ryan & Killick (2023) <doi:10.1080/00401706.2023.2183261> and a novel estimation methodology from Fotopoulos et al. (2023) <doi:10.1007/s00362-023-01495-0> generalized to fit the detection methodologies. Performs both detection and estimation of change points, providing visualization and summary information of the estimation process for each detected change point.

Functions, methods, and datasets for fitting dimension reduction regression, using slicing (methods SAVE and SIR), Principal Hessian Directions (phd, using residuals and the response), and an iterative IRE. Partial methods, that condition on categorical predictors are also available. A variety of tests, and stepwise deletion of predictors, is also included. Also included is code for computing permutation tests of dimension. Adding additional methods of estimating dimension is straightforward. For documentation, see the vignette in the package. With version 3.0.4, the arguments for dr.step have been modified.

This package provides a function for plotting maps of agricultural field experiments that are laid out in grids. See Ryder (1981) <doi:10.1017/S0014479700011601>.

This package implements discrete curvature estimation for ordered planar point sequences using circumcenter geometry on consecutive triplets, exposed through compiled C plus plus (C++) code via Rcpp for speed and numerical robustness. The package is useful for objective elbow detection in multivariate workflows, especially principal component analysis (PCA), where selecting the number of retained components can be subjective. It provides a shiny interface that supports upload of raw datasets or explained-variance tables, computes Kaiser-Meyer-Olkin (KMO) sampling-adequacy diagnostics, evaluates individual and cumulative variance curves, and reports curvature- based decision rules (m* and m**) with visual summaries for reproducible component-selection decisions. References: Arney et al. (2001); Axler (2024) <doi:10.1007/978-3-031-41026-0>; Bjorklund (2019) <doi:10.1111/evo.13835>; Burden and Faires (2015); Chang et al. (2023) <https://CRAN.R-project.org/package=shiny>; Christensen (2019); Cui (2020) <doi:10.18637/jss.v040.i08>; Eddelbuettel and Sanderson (2014) <doi:10.1016/j.csda.2013.02.005>; Engelke et al. (2023) <doi:10.1016/j.jseint.2023.04.010>; Gniazdowski (2021) <doi:10.26348/znwwsi.24.35>; Haynes et al. (2017); Jameel and Al-Salami (2023) <doi:10.24086/cuejhss.v7n1y2023.pp121-125>; Jolliffe (2002); Jolliffe and Cadima (2016) <doi:10.1098/rsta.2015.0202>; Kaiser (1974); Lehnert et al. (2019) <doi:10.18637/jss.v089.i12>; Ma and Dai (2011) <doi:10.1093/bib/bbq090>; Milligan (1995); Onumanyi et al. (2022) <doi:10.3390/app12157515>; Park (2010); Revelle (2024) <https://CRAN.R-project.org/package=psych>; Rodionova et al. (2021) <doi:10.1016/j.chemolab.2021.104304>; Sen and Cohen (2025) <doi:10.1177/01466216251344288>; Serneels and Verdonck (2008) <doi:10.1016/j.csda.2007.05.024>; Shi et al. (2021) <doi:10.1186/s13638-021-01910-w>; Shaukat et al. (2016) <doi:10.1515/eko-2016-0014>; Syakur et al. (2018) <doi:10.1088/1757-899X/336/1/012017>; Wickham and Bryan (2023) <https://CRAN.R-project.org/package=readxl>; Wu et al. (2017) <doi:10.1088/1755-1315/61/1/012054>; Youssef et al. (2023) <doi:10.21303/2461-4262.2023.002582>.

Intelligently assign samples to batches in order to reduce batch effects. Batch effects can have a significant impact on data analysis, especially when the assignment of samples to batches coincides with the contrast groups being studied. By defining a batch container and a scoring function that reflects the contrasts, this package allows users to assign samples in a way that minimizes the potential impact of batch effects on the comparison of interest. Among other functionality, we provide an implementation for OSAT score by Yan et al. (2012, <doi:10.1186/1471-2164-13-689>).

Data frame, tibble, or tbl objects are converted to data package objects using specific metadata labels (name, version, title, homepage, description). A data package object ('dpkg') can be written to disk as a parquet file or released to a GitHub repository. Data package objects can be read into R from online repositories and downloaded files are cached locally across R sessions.

Reverse and model the effects of changing deposition rates on geological data and rates. Based on Hohmann (2018) <doi:10.13140/RG.2.2.23372.51841> .

Shiny application that performs bifurcation and phaseplane analysis of systems of ordinary differential equations. The package allows for computation of equilibrium curves as a function of a single free parameter, detection of transcritical, saddle-node and hopf bifurcation points along these curves, and computation of curves representing these transcritical, saddle-node and hopf bifurcation points as a function of two free parameters. The shiny-based GUI allows visualization of the results in both 2D- and 3D-plots. The implemented methods for solution localisation and curve continuation are based on the book "Elements of applied bifurcation theory" (Kuznetsov, Y. A., 1995; ISBN: 0-387-94418-4).

Supporting the quantitative analysis of binary welfare based decision making processes using Monte Carlo simulations. Decision support is given on two levels: (i) The actual decision level is to choose between two alternatives under probabilistic uncertainty. This package calculates the optimal decision based on maximizing expected welfare. (ii) The meta decision level is to allocate resources to reduce the uncertainty in the underlying decision problem, i.e to increase the current information to improve the actual decision making process. This problem is dealt with using the Value of Information Analysis. The Expected Value of Information for arbitrary prospective estimates can be calculated as well as Individual Expected Value of Perfect Information. The probabilistic calculations are done via Monte Carlo simulations. This Monte Carlo functionality can be used on its own.

Simple feature stores and tools for creating personalised feature stores. diseasystore powers feature stores which can automatically link and aggregate features to a given stratification level. These feature stores are automatically time-versioned (powered by the SCDB package) and allows you to easily and dynamically compute features as part of your continuous integration.

Efficient covariate-adjusted estimators of quantities that are useful for establishing the effects of treatments on ordinal outcomes.

Mapping, spatial analysis, and statistical modeling of microdata from sources such as the Demographic and Health Surveys <https://www.dhsprogram.com/> and Integrated Public Use Microdata Series <https://www.ipums.org/>. It can also be extended to other datasets. The package supports spatial correlation index construction and visualization, along with empirical Bayes approximation of regression coefficients in a multistage setup. The main functionality is repeated regression â for example, if we have to run regression for n groups, the group ID should be vertically composed into the variable for the parameter `location_var`. It can perform various kinds of regression, such as Generalized Regression Models, logit, probit, and more. Additionally, it can incorporate interaction effects. The key benefit of the package is its ability to store the regression results performed repeatedly on a dataset by the group ID, along with respective p-values and map those estimates.

Implementations of several multiple testing procedures that control the family-wise error rate (FWER) designed specifically for discrete tests. Included are discrete adaptations of the Bonferroni, Holm, Hochberg and Å idák procedures as described in the papers Döhler (2010) "Validation of credit default probabilities using multiple-testing procedures" <doi:10.21314/JRMV.2010.062> and Zhu & Guo (2019) "Family-Wise Error Rate Controlling Procedures for Discrete Data" <doi:10.1080/19466315.2019.1654912>. The main procedures of this package take as input the results of a test procedure from package DiscreteTests or a set of observed p-values and their discrete support under their nulls. A shortcut function to apply discrete procedures directly to data is also provided.

Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.

This package performs detection of Differential Item Functioning using the method DIFboost as proposed by Schauberger and Tutz (2016) <doi:10.1111/bmsp.12060>.

Description of statistical associations between variables : measures of local and global association between variables (phi, Cramér V, correlations, eta-squared, Goodman and Kruskal tau, permutation tests, etc.), multiple graphical representations of the associations between variables (using ggplot2') and weighted statistics.

Interface for Rcpp users to dlib <http://dlib.net> which is a C++ toolkit containing machine learning algorithms and computer vision tools. It is used in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. This package allows R users to use dlib through Rcpp'.

This package creates discretised versions of continuous distribution functions by mapping continuous values to an underlying discrete grid, based on a (uniform) frequency of discretisation, a valid discretisation point, and an integration range. For a review of discretisation methods, see Chakraborty (2015) <doi:10.1186/s40488-015-0028-6>.

This package provides a management tool for specimen data ranging from public museum collections to private specimen repositories. The main types of data addressed are spatial (coordinates, longitude and latitude) and taxonomic data (ranking and nomenclature validity) with some additional options for user-determined dataset refinement. Combined or individual calls to the online repositories of the Global Biodiversity Information Facility (GBIF) via rgbif and the Integrated Taxonomic Information System (ITIS) via taxize enable built-in taxonomic checks.