Makes it easy to install and load a collection of packages for survey analysis that build upon the foundational survey package of Lumley (2004) <doi:10.18637/jss.v009.i08>. Schneider (2025) <https://isi-iass.org/home/wp-content/uploads/Survey_Statistician_2025_January_N91_06.pdf> describes the three core packages in this collection.

Construct various types of space-filling designs, including Latin hypercube designs, clustering-based designs, maximin designs, maximum projection designs, and uniform designs (Joseph 2016 <doi:10.1080/08982112.2015.1100447>). It also offers the option to optimize designs based on user-defined criteria. This work is supported by U.S. National Science Foundation grant DMS-2310637.

This package provides functions for converting transliterated Sumerian texts to sign names and cuneiform characters, creating and querying dictionaries, analyzing the structure of Sumerian words, and creating translations. Includes a built-in dictionary and supports both forward lookup (Sumerian to English) and reverse lookup (English to Sumerian).

Herein, we provide a broad variety of functions which are useful for handling, manipulating, and visualizing satellite-based remote sensing data. These operations range from mere data import and layer handling (eg subsetting), over Raster* typical data wrangling (eg crop, extend), to more sophisticated (pre-)processing tasks typically applied to satellite imagery (eg atmospheric and topographic correction). This functionality is complemented by a full access to the satellite layers metadata at any stage and the documentation of performed actions in a separate log file. Currently available sensors include Landsat 4-5 (TM), 7 (ETM+), and 8 (OLI/TIRS Combined), and additional compatibility is ensured for the Landsat Global Land Survey data set.

User-friendly functions which parse output of command line programs used to query Slurm. Morris A. Jette and Tim Wickberg (2023) <doi:10.1007/978-3-031-43943-8_1> describe Slurm in detail.

Framework to build an individual tree simulator.

This package provides modular functions and applications for quickly generating plots and tables. Each modular function opens a graphical user interface providing the user with options to create and customise plots and tables.

This package provides a framework for evaluating drug combination effects in preclinical in vivo studies. SynergyLMM provides functions to analyze longitudinal tumor growth experiments using mixed-effects models, perform time-resolved analyses of synergy and antagonism, evaluate model diagnostics and performance, and assess both post-hoc and a priori statistical power. The calculation of drug combination synergy follows the statistical framework provided by Demidenko and Miller (2019, <doi:10.1371/journal.pone.0224137>). The implementation and analysis of linear mixed-effect models is based on the methods described by Pinheiro and Bates (2000, <doi:10.1007/b98882>), and GaÅ ecki and Burzykowski (2013, <doi:10.1007/978-1-4614-3900-4>).

This package provides a pipeline for estimating the stellar age, mass, and radius given observational effective temperature, [Fe/H], and astroseismic parameters. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models, as described in Valle et al. (2014) <doi:10.1051/0004-6361/201322210>.

Select sampling methods for probability samples using large data sets. This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer.

This package provides a process-oriented and trajectory-based Discrete-Event Simulation (DES) package for R. It is designed as a generic yet powerful framework. The architecture encloses a robust and fast simulation core written in C++ with automatic monitoring capabilities. It provides a rich and flexible R API that revolves around the concept of trajectory, a common path in the simulation model for entities of the same type. Documentation about simmer is provided by several vignettes included in this package, via the paper by Ucar, Smeets & Azcorra (2019, <doi:10.18637/jss.v090.i02>), and the paper by Ucar, Hernández, Serrano & Azcorra (2018, <doi:10.1109/MCOM.2018.1700960>); see citation("simmer") for details.

Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).

This package provides deep learning models for right-censored survival data using the torch backend. Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>. The package is described in El Badisy (2026) <doi:10.32614/RJ-2026-008>.

Spatiotemporal individual-level model of seasonal infectious disease transmission within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model seasonal infectious disease transmission. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. In addition to model fitting and parameter estimation, the package offers functions for calculating AIC using real pandemic data and conducting simulation studies customized to user-specified model configurations.

Two-step and maximum likelihood estimation of Heckman-type sample selection models: standard sample selection models (Tobit-2), endogenous switching regression models (Tobit-5), sample selection models with binary dependent outcome variable, interval regression with sample selection (only ML estimation), and endogenous treatment effects models. These methods are described in the three vignettes that are included in this package and in econometric textbooks such as Greene (2011, Econometric Analysis, 7th edition, Pearson).

This package provides functions to combine, normalize and visualize spectral data, perform principal component analysis (PCA), and assemble customizable image grids suitable for publication-quality scientific figures.

Automated unit testing of Shiny applications through a headless Chromium browser.

Compare directories flexibly (by date, content, or both) and synchronize files efficiently, with asymmetric and symmetric modes, helper tools, and visualization support for file management.

An R shiny user interface for the nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>) package, designed to simplify the modeling process for users. Additionally, this package includes supplementary functions to further enhances the usage of nlmixr2'.

Simulation tools for planning Vitamin D studies. Individual vitamin D status profiles are simulated, modelling population heterogeneity in trial arms. Exposures to infectious agents are generated, with infection depending on vitamin D status.

Estimates cohort-by-time average treatment effects under staggered treatment adoption by the generalized method of moments. Three weighting schemes are provided, corresponding to a pooled stationary covariance, a cohort-specific stationary covariance, and an unrestricted within-cohort covariance. Optional adjustment for baseline covariates by outcome regression, and a serial-correlation robust over-identification test of parallel trends and no anticipation, are also supported. The methods are described in Arora and Bijani (2026) <doi:10.2139/ssrn.6558759>.

This package provides functions to perform most of the common analysis in genome association studies are implemented. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated. The methods are described in Gonzalez JR et al., 2007 <doi: 10.1093/bioinformatics/btm025>. This version includes internal copies of functions from the archived haplo.stats package to maintain functionality.

Provide utilities to work with solar time, i.e. where noon is exactly when sun culminates. Provides functions for computing sun position and times of sunrise and sunset.

Access Amazon Web Service Simple Storage Service ('S3') <https://aws.amazon.com/s3/> as if it were a file system. Interface based on the R package fs'.