Feature selection using Sequential Forward Floating feature Selection and Jeffries-Matusita distance. It returns a suboptimal set of features to use for image classification. Reference: Dalponte, M., Oerka, H.O., Gobakken, T., Gianelle, D. & Naesset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51, 2632-2645, <DOI:10.1109/TGRS.2012.2216272>.

This package provides functions for validating the structure and properties of data frames. Answers essential questions about a data set after initial import or modification. What are the unique or missing values? What columns form a primary key? What are the properties of the numeric or categorical columns? What kind of overlap or mapping exists between 2 columns?

This package provides a set of basic tools to transform functions into functions with input validation checks, in a manner suitable for both programmatic and interactive use.

This package provides a collection of functions to make R a more effective viewscape analysis tool for calculating viewscape metrics based on computing the viewable area for given a point/multiple viewpoints and a digital elevation model.The method of calculating viewscape metrics implemented in this package are based on the work of Tabrizian et al. (2020) <doi:10.1016/j.landurbplan.2019.103704>. The algorithm of computing viewshed is based on the work of Franklin & Ray. (1994) <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=555780f6f5d7e537eb1edb28862c86d1519af2be>.

Speech-to-text transcription using a native R torch implementation of OpenAI Whisper model <https://github.com/openai/whisper>. Supports multiple model sizes from tiny (39M parameters) to large-v3 (1.5B parameters) with integrated download from HuggingFace <https://huggingface.co/> via the hfhub package. Provides automatic speech recognition with optional language detection and translation to English. Audio preprocessing, mel spectrogram computation, and transformer-based encoder-decoder inference are all implemented in R using the torch package.

Conducts single coefficient tests and multiple-contrast hypothesis tests of meta-regression models using cluster wild bootstrapping, based on methods examined in Joshi, Pustejovsky, and Beretvas (2022) <DOI:10.1002/jrsm.1554>.

This package performs an analysis of time-to-event clinical trial data using various "win time" methods, including ewt', ewtr', rmt', ewtp', rewtp', ewtpr', rewtpr', max', wtr', rwtr', pwt', and rpwt'. These methods are used to calculate and compare treatment effects on ordered composite endpoints. The package handles event times, event indicators, and treatment arm indicators and supports calculations on observed and resampled data. Detailed explanations of each method and usage examples are provided in "Use of win time for ordered composite endpoints in clinical trials," by Troendle et al. (2024)<https://pubmed.ncbi.nlm.nih.gov/38417455/>. For more information, see the package documentation or the vignette titled "Introduction to wintime.".

Computation of the Wasserstein Bipolarization Index as described in Lee and Sobel (Forthcoming) <doi:10.48550/arXiv.2408.03331>. Provides both asymptotic (Sommerfeld, 2017 <https://ediss.uni-goettingen.de/bitstream/handle/11858/00-1735-0000-0023-3FA1-C/DissertationSommerfeldRev.pdf?sequence=1>) and bootstrap methods (Efron and Narasimhan, 2020 <doi:10.1080/10618600.2020.1714633>) for calculating confidence intervals.

Computes exact observation weights for the Kalman filter and smoother, following Koopman and Harvey (2003) <www.sciencedirect.com/science/article/pii/S0165188902000611>. The package provides tools for analyzing linear Gaussian state-space models, allowing users to quantify the contribution of individual observations to filtered and smoothed state estimates. These weights can be used for interpretation, decomposition, and diagnostic analysis in time series models, including applications such as dynamic factor models. See the README for examples.

Analyze given data frame with multiple endpoints and return Kaplan-Meier survival probabilities together with the specified confidence interval. See Nabipoor M, Westerhout CM, Rathwell S, and Bakal JA (2023) <doi:10.1186/s12874-023-01857-0>.

This package provides insight into how the best hand for a poker game changes based on the game dealt, players who stay in until the showdown and wildcards added to the base game. At this time the package does not support player tactics, so draw poker variants are not included.

Search and download data from the World Bank Data API.

This estimates precise weaning ages for a given skeletal population by analyzing the stable nitrogen isotope ratios of them. Bone collagen turnover rates estimated anew and the approximate Bayesian computation (ABC) were adopted in this package.

This package provides a WebSocket client interface for R. WebSocket is a protocol for low-overhead real-time communication: <https://en.wikipedia.org/wiki/WebSocket>.

This package provides a multivariate weather generator for daily climate variables based on weather-states (Flecher et al. (2010) <doi:10.1029/2009WR008098>). It uses a Markov chain for modeling the succession of weather states. Conditionally to the weather states, the multivariate variables are modeled using the family of Complete Skew-Normal distributions. Parameters are estimated on measured series. Must include the variable Rain and can accept as many other variables as desired.

Formal implementation of White test of heteroskedasticity and a bootstrapped version of it, developed under the methodology of Jeong, J., Lee, K. (1999) <https://yonsei.pure.elsevier.com/en/publications/bootstrapped-whites-test-for-heteroskedasticity-in-regression-mod>.

Extract features and classify documents with noisy labels given by document-meta data or keyword matching Watanabe & Zhou (2020) <doi:10.1177/0894439320907027>.

An enhanced implementation of Whittaker-Henderson smoothing for the graduation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. WH is based on the methods described in Biessy (2025) <doi:10.48550/arXiv.2306.06932>. Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data.

Taxonomic information from Wikipedia', Wikicommons', Wikispecies', and Wikidata'. Functions included for getting taxonomic information from each of the sources just listed, as well performing taxonomic search.

This package provides a convenient data set, a set of helper functions, and a benchmark function for economically (profit) driven wind farm layout optimization. This enables researchers in the field of the NP-hard (non-deterministic polynomial-time hard) problem of wind farm layout optimization to focus on their optimization methodology contribution and also provides a realistic benchmark setting for comparability among contributions. See Croonenbroeck, Carsten & Hennecke, David (2020) <doi:10.1016/j.energy.2020.119244>.

R clients to the Web of Science and InCites <https://clarivate.com/products/data-integration/> APIs, which allow you to programmatically download publication and citation data indexed in the Web of Science and InCites databases.

Treemaps are a visually appealing graphical representation of numerical data using a space-filling approach. A plane or map is subdivided into smaller areas called cells. The cells in the map are scaled according to an underlying metric which allows to grasp the hierarchical organization and relative importance of many objects at once. This package contains two different implementations of treemaps, Voronoi treemaps and Sunburst treemaps. The Voronoi treemap function subdivides the plot area in polygonal cells according to the highest hierarchical level, then continues to subdivide those parental cells on the next lower hierarchical level, and so on. The Sunburst treemap is a computationally less demanding treemap that does not require iterative refinement, but simply generates circle sectors that are sized according to predefined weights. The Voronoi tesselation is based on functions from Paul Murrell (2012) <https://www.stat.auckland.ac.nz/~paul/Reports/VoronoiTreemap/voronoiTreeMap.html>.

This package provides functions to import data from more than 30,000 surface meteorological sites around the world managed by the National Oceanic and Atmospheric Administration (NOAA) Global Historical Climate Network (GHCN) and Integrated Surface Database (ISD).

Estimates the Vevea and Hedges (1995) weight-function model. By specifying arguments, users can also estimate the modified model described in Vevea and Woods (2005), which may be more practical with small datasets. Users can also specify moderators to estimate a linear model. The package functionality allows users to easily extract the results of these analyses as R objects for other uses. In addition, the package includes a function to launch both models as a Shiny application. Although the Shiny application is also available online, this function allows users to launch it locally if they choose.