This package provides a dplyr'-like interface for grouped summaries on large arrow datasets. Complete summaries are attempted in arrow first. When a result can be reconstructed from arrow'-computable sufficient statistics, an explicit MapReduce-style reduction and R finalization strategy is available. For arbitrary R functions that require raw group observations, complete groups are materialized in bounded chunks, with optional parallel execution and shared-memory processing. The MapReduce strategy follows the programming model described by Dean and Ghemawat (2008) <doi:10.1145/1327452.1327492>.

High-performance spatial species accumulation curves using fixed-focus spatially constrained rarefaction, nearest-centroid expansion, and nearest-neighbor walks with a C++ backend for speed. Supports Hill numbers (q=0,1,2), beta diversity partitioning (turnover/nestedness) at site and pooled resolution, coverage-based rarefaction and extrapolation of alpha and beta diversity, phylogenetic diversity (PD; Faith's PD, EvoHeritage, mean pairwise distance, mean nearest taxon distance), functional diversity accumulation including aggregated functional diversity and intraspecific trait variation, phylogenetic and functional redundancy, interaction network accumulation, diversity-area relationships (DAR), endemism-area curves, sampling-effort correction and fragmentation analysis, species-area relationship (SAR) models based on extreme value theory (EVT), and area-based richness extrapolation via the total-species curve. Total richness is also estimated from frequency counts, including the accumulation rate curve estimator and an estimator for sites sampled without replacement, and shift-and-rotate null models test the position of a sampling design. Multiple starting points (seeds) provide uncertainty quantification. Methods are described in Chao et al. (2014) <doi:10.1890/13-0133.1>, Baselga (2010) <doi:10.1111/j.1466-8238.2009.00490.x>, Chao and Jost (2012) <doi:10.1890/11-1952.1>, Faith (1992) <doi:10.1016/0006-3207(92)91201-3>, Ma (2018) <doi:10.1002/ece3.4425>, Borda-de-Agua et al. (2025) <doi:10.1038/s41467-025-59239-7>, Hanski et al. (2013) <doi:10.1073/pnas.1311190110>, Jost (2007) <doi:10.1890/06-1736.1>, Ugland et al. (2003) <doi:10.1046/j.1365-2656.2003.00748.x>, Chao et al. (2023) <doi:10.1002/ecm.1588>, Chiu (2023) <doi:10.1111/2041-210X.14146>, Chiu et al. (2023) <doi:10.1098/rstb.2022.0183>, Rosindell et al. (2024) <doi:10.1093/sysbio/syad072>, Wojcik et al. (2024) <doi:10.1111/2041-210X.14470>, Ridder et al. (2024) <doi:10.1111/2041-210X.14443>, Jones et al. (2025) <doi:10.1111/ddi.70080>, and Shestopaloff (2025) <doi:10.1007/s10651-025-00644-y>.

This package provides intuitive functions for caching R objects, encouraging reproducible, restartable, and distributed R analysis. The user selects a location to store caches, and then provides nothing more than a cache name and instructions (R code) for how to produce the R object. Also provides some advanced options like environment assignments, recreating or reloading caches, and cluster compute bindings (using the batchtools package) making it flexible enough for use in large-scale data analysis projects.

The public available Danish SKS (Sundhedsvæsenets Klassifikationssystem) and SNOMED (Systematized Nomenclature of Medicine) codes with labels. The labels are useful to sort and make sense of microdata in closed computing environments. Current as of april 2026 (SNOMED) and june 2026 (SKS). SNOMED <https://www.patobank.dk/snomed/> and SKS <https://filer.sundhedsdata.dk/sks/data/skscomplete/>.

This package provides a programmatic interface to <http://sp2000.org.cn>, re-written based on an accompanying Species 2000 API. Access tables describing catalogue of the Chinese known species of animals, plants, fungi, micro-organisms, and more. This package also supports access to catalogue of life global <http://catalogueoflife.org>, China animal scientific database <http://zoology.especies.cn> and catalogue of life Taiwan <https://taibnet.sinica.edu.tw/home_eng.php>. The development of SP2000 package were supported by Biodiversity Survey and Assessment Project of the Ministry of Ecology and Environment, China <2019HJ2096001006>,Yunnan University's "Double First Class" Project <C176240405> and Yunnan University's Research Innovation Fund for Graduate Students <2019227>.

Three new methods to perform outlier detection in a survival context. In total there are six methods provided, the first three methods are traditional residual-based outlier detection methods, the second three are the concordance-based. Package developed during the work on the two following publications: Pinto J., Carvalho A. and Vinga S. (2015) <doi:10.5220/0005225300750082>; Pinto J.D., Carvalho A.M., Vinga S. (2015) <doi:10.1007/978-3-319-27926-8_22>.

This package provides tools for the simulation of data in the context of small area estimation. Combine all steps of your simulation - from data generation over drawing samples to model fitting - in one object. This enables easy modification and combination of different scenarios. You can store your results in a folder or start the simulation in parallel.

Provision of the S4 SpatialGraph class built on top of objects provided by igraph and sp packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg().

Fits sparse linear varying coefficient models (VCMs), which assert a linear relationship between an outcome and several covariates that is allowed to change as functions of additional variables known as effect modifiers. Designed for high-dimensional settings where the number of covariates (i.e., number of slopes) is comparable to or larger than the number of observations. Approximates the coefficient functions using a version of Bayesian Additive Regression Trees that can perform global-local shrinkage. For more details see Ghosh, Bhogale, and Deshpande (2026+) <doi:10.48550/arXiv.2510.08204>.

This package provides a combined slider and numeric input for usage in a Shiny app. The slider and the numeric input are linked together: each one is updated when the other one changes. Many styling properties are customizable (e.g. colors and size).

Sequential, anytime-valid comparison of algorithms on paired losses with a practical-equivalence margin. Confidence sequences for the mean paired difference, superiority/equivalence/continue decisions, cost accounting and auditable trajectories.

Create mixed models with repeated measures using natural cubic splines applied to an observed continuous time variable, as described by Donohue et al. (2023) <doi:10.1002/pst.2285>. Iterate through multiple covariance structure types until one converges. Categorize observed time according to scheduled visits. Perform subgroup analyses.

This package contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.

This package provides utilities for conducting specification curve analyses (Simonsohn, Simmons & Nelson, 2020, <doi:10.1038/s41562-020-0912-z>) or multiverse analyses (Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016, <doi:10.1177/1745691616658637>) including functions to setup, run, evaluate, and plot all specifications, as well as to conduct inference on the specification curve based on resampling under the null.

This is a collection of various kinds of data with broad uses for teaching. My students, and academics like me who teach the same topics I teach, should find this useful if their teaching workflow is also built around the R programming language. The applications are multiple but mostly cluster on topics of statistical methodology, international relations, and political economy.

This package provides a collection of self-labeled techniques for semi-supervised classification. In semi-supervised classification, both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. This package implements a collection of self-labeled techniques to construct a classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented can be applied to classification problems in several domains by the specification of a supervised base classifier. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.

Data related to the Salem Witch Trials Datasets and tutorials documenting the witch accusations and trials centered around Salem, Massachusetts in 1692. Originally assembled by Richard B. Latner of Tulane University for his website <https://www2.tulane.edu/~salem/index.html>. The data sets include information on 152 accused witches, members of the Salem Village Committee, signatories of petitions related to the events, and tax data for Salem Village.

This package implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2023) <doi:10.48550/arXiv.2308.04325>, and allows for the statistical modeling of asymmetric between-location effects, as well as within-location effects using spatial autoregressive graphical models. The package allows for the generation of spatial weight matrices to capture asymmetric effects for strip-type intercropping designs, although it can handle any type of spatial data commonly found in other sciences.

This package provides some basic routines for simulating a clinical trial. The primary intent is to provide some tools to generate trial simulations for trials with time to event outcomes. Piecewise exponential failure rates and piecewise constant enrollment rates are the underlying mechanism used to simulate a broad range of scenarios such as those presented in Lin et al. (2020) <doi:10.1080/19466315.2019.1697738>. However, the basic generation of data is done using pipes to allow maximum flexibility for users to meet different needs.

This package provides a collection of functions to search and download street view imagery ('Mapilary <https://www.mapillary.com/developer/api-documentation>) and to extract, quantify, and visualize visual features. Moreover, there are functions provided to generate Qualtrics survey in TXT format using the collection of street views for various research purposes.

The synchrosqueezed wavelet transform is implemented. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.

This package provides functions that wrap HTML Bootstrap components code to enable the design and layout of informative landing home pages for Shiny applications. This can lead to a better user experience for the users and writing less HTML for the developer.

This package provides functions are provided for the density function, distribution function, quantiles and random number generation for the skew hyperbolic t-distribution. There are also functions that fit the distribution to data. There are functions for the mean, variance, skewness, kurtosis and mode of a given distribution and to calculate moments of any order about any centre. To assess goodness of fit, there are functions to generate a Q-Q plot, a P-P plot and a tail plot.

This package implements five factor extraction methods for asset pricing and macroeconomic forecasting: principal component analysis (PCA), partial least squares (PLS), scaled PCA (sPCA) of Huang, Jiang, Li, Tong, and Zhou (2022) <doi:10.1287/mnsc.2021.4020>, the reduced-rank approach (RRA) of He, Huang, Li, and Zhou (2023) <doi:10.1287/mnsc.2022.4563>, and Instrumented PCA (IPCA) of Kelly, Pruitt, and Su (2019) <doi:10.1016/j.jfineco.2019.05.001>.