The tools in this package are intended to help researchers assess multiple treatment-covariate interactions with data from a parallel-group randomized controlled clinical trial. The methods implemented in the package were proposed in Kovalchik, Varadhan and Weiss (2013) <doi: 10.1002/sim.5881>.

Compute an anomaly score for multivariate time series based on the k-nearest neighbors algorithm. Different computations of distances between time series are provided.

Enables gene regulatory network (GRN) analysis on single cell clusters, using the GRN analysis software ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>. Export data from Seurat objects, for GRN analysis by ANANSE implemented in snakemake'. Finally, incorporate results for visualization and interpretation.

The agghoo procedure is an alternative to usual cross-validation. Instead of choosing the best model trained on V subsamples, it determines a winner model for each subsample, and then aggregates the V outputs. For the details, see "Aggregated hold-out" by Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890> published in Journal of Machine Learning Research 22(20):1--55.

Utilities to parse authors fields from DESCRIPTION files and general purpose functions to deduplicate names in database, beyond the specific case of R package authors.

This package provides a collection of lightweight functions that can be used to determine the computing environment in which your code is running. This includes operating systems, continuous integration (CI) environments, containers, and more.

Assess whether and how a specific continuous or categorical exposure affects the outcome of interest through one- or multi-dimensional mediators using an adaptive bootstrap (AB) approach. The AB method allows to make inference for composite null hypotheses of no mediation effect, providing valid type I error control and thus optimizes statistical power. For more technical details, refer to He, Song and Xu (2024) <doi:10.1093/jrsssb/qkad129>.

Read, manipulate and write voxel spaces. Voxel spaces are read from text-based output files of the AMAPVox software. AMAPVox is a LiDAR point cloud voxelisation software that aims at estimating leaf area through several theoretical/numerical approaches. See more in the article Vincent et al. (2017) <doi:10.23708/1AJNMP> and the technical note Vincent et al. (2021) <doi:10.23708/1AJNMP>.

Allows the user to implement an address search auto completion menu on shiny text inputs. This is done using the Algolia Places JavaScript library. See <https://community.algolia.com/places/>.

Geographic, use, and property related data on airports.

Flexible parametric Accelerated Hazards (AH) regression models in overall and relative survival frameworks with 13 distinct Baseline Distributions. The AH Model can also be applied to lifetime data with crossed survival curves. Any user-defined parametric distribution can be fitted, given at least an R function defining the cumulative hazard and hazard rate functions. See Chen and Wang (2000) <doi:10.1080/01621459.2000.10474236>, and Lee (2015) <doi:10.1007/s10985-015-9349-5> for more details.

This package provides easy access to the AviList Global Avian Checklist, the first unified global bird taxonomy that harmonizes previous differences between International Ornithological Committee ('IOC'), Clements', and BirdLife checklists. This package contains the complete AviList dataset as R data objects ready for ornithological research and analysis. For more details see AviList Core Team (2025) <doi:10.2173/avilist.v2025>.

Exploration of Weather Research & Forecasting ('WRF') Model data of Servicio Meteorologico Nacional (SMN) from Amazon Web Services (<https://registry.opendata.aws/smn-ar-wrf-dataset/>) cloud. The package provides the possibility of data downloading, processing and correction methods. It also has map management and series exploration of available meteorological variables of WRF forecast.

An interface to Azure Cognitive Services <https://learn.microsoft.com/en-us/azure/cognitive-services/>. Both an Azure Resource Manager interface, for deploying Cognitive Services resources, and a client framework are supplied. While AzureCognitive can be called by the end-user, it is meant to provide a foundation for other packages that will support specific services, like Computer Vision, Custom Vision, language translation, and so on. Part of the AzureR family of packages.

Calculate ActiGraph counts from the X, Y, and Z axes of a triaxial accelerometer. This work was inspired by Neishabouri et al. who published the article "Quantification of Acceleration as Activity Counts in ActiGraph Wearables" on February 24, 2022. The link to the article (<https://pubmed.ncbi.nlm.nih.gov/35831446>) and python implementation of this code (<https://github.com/actigraph/agcounts>).

Functions, data sets and examples for the calculation of various indices of biodiversity including species, functional and phylogenetic diversity. Part of the indices are expressed in terms of equivalent numbers of species. The package also provides ways to partition biodiversity across spatial or temporal scales (alpha, beta, gamma diversities). In addition to the quantification of biodiversity, ordination approaches are available which rely on diversity indices and allow the detailed identification of species, functional or phylogenetic differences between communities.

This package provides a collection of tools to deal with raster maps.

This package provides new_partialised() and new_composed(), which extend partial() and compose() functions of purrr to make it easier to extract and replace arguments and functions. It also has additional adverbial functions.

Modern software often poorly support older file formats. This package intends to handle many file formats that were native to the antiquated Commodore Amiga machine. This package focuses on file types from the older Amiga operating systems (<= 3.0). It will read and write specific file formats and coerces them into more contemporary data.

Routines for re-scaling isotope maps using known-origin tissue isotope data, assigning origin of unknown samples, and summarizing and assessing assignment results. Methods are adapted from Wunder (2010, in ISBN:9789048133536) and Vander Zanden, H. B. et al. (2014) <doi:10.1111/2041-210X.12229> as described in Ma, C. et al. (2020) <doi:10.1111/2041-210X.13426>.

Empirical likelihood-based approximate Bayesian Computation. Approximates the required posterior using empirical likelihood and estimated differential entropy. This is achieved without requiring any specification of the likelihood or estimating equations that connects the observations with the underlying parameters. The procedure is known to be posterior consistent. More details can be found in Chaudhuri, Ghosh, and Kim (2024) <doi:10.1002/SAM.11711>.

The adapted pair correlation function transfers the concept of the pair correlation function from point patterns to patterns of objects of finite size and irregular shape (e.g. lakes within a country). The pair correlation function describes the spatial distribution of objects, e.g. random, aggregated or regularly spaced. This is a reimplementation of the method suggested by Nuske et al. (2009) <doi:10.1016/j.foreco.2009.09.050> using the library GEOS <doi:10.5281/zenodo.11396894>.

Collect your data on digital marketing campaigns from Adform Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package implements binomial tree pricing for geometric and arithmetic Asian options incorporating market price impact from hedging activities. Uses the Cox-Ross-Rubinstein (CRR) model with the replicating portfolio method. Provides exact pricing for geometric Asian options and bounds for arithmetic Asian options based on Jensen's inequality. The price impact mechanism models how hedging volumes affect stock prices, leading to modified risk-neutral probabilities. Based on the methodology described in Tiwari and Majumdar (2025) <doi:10.48550/arXiv.2512.07154>.