Provide functions for forest inventory calculations. Common volumetric equations (Smalian, Newton and Huber) as well stacking factor and form.

Generates RProtobuf classes for FactSet STACH V2 tabular format which represents complex multi-dimensional array of data. These classes help in the serialization and deserialization of STACH V2 formatted data. See GitHub repository documentation for more information.

Generate cost effective minimally changed run sequences for symmetrical as well as asymmetrical factorial designs.

This package provides a collection of utility functions for working with Year Month Day objects. Includes functions for fast parsing of numeric and character input based on algorithms described in Hinnant, H. (2021) <https://howardhinnant.github.io/date_algorithms.html> as well as a branchless calculation of leap years by Jerichaux (2025) <https://stackoverflow.com/a/79564914>.

Quickly make tables of descriptive statistics (i.e., counts, percentages, confidence intervals) for categorical variables. This package is designed to work in a Tidyverse pipeline, and consideration has been given to get results from R to Microsoft Word ® with minimal pain.

Fits Weibull or sigmoidal models to percent loss conductivity (plc) curves as a function of plant water potential, computes confidence intervals of parameter estimates and predictions with bootstrap or parametric methods, and provides convenient plotting methods.

This is an extremely fast implementation of a Naive Bayes classifier. This package is currently the only package that supports a Bernoulli distribution, a Multinomial distribution, and a Gaussian distribution, making it suitable for both binary features, frequency counts, and numerical features. Another feature is the support of a mix of different event models. Only numerical variables are allowed, however, categorical variables can be transformed into dummies and used with the Bernoulli distribution. The implementation is largely based on the paper "A comparison of event models for Naive Bayes anti-spam e-mail filtering" written by K.M. Schneider (2003) <doi:10.3115/1067807.1067848>. Any issues can be submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.

Computes likelihood ratios based on pigmentation traits. Also, it allows computing conditional probabilities for unidentified individuals based on missing person characteristics. A set of tailored plots are incorporated to analyze likelihood ratio distributions.

An implementation of the fair data adaptation with quantile preservation described in Plecko & Meinshausen (JMLR 2020, 21(242), 1-44). The adaptation procedure uses the specified causal graph to pre-process the given training and testing data in such a way to remove the bias caused by the protected attribute. The procedure uses tree ensembles for quantile regression. Instructions for using the methods are further elaborated in the corresponding JSS manuscript, see <doi:10.18637/jss.v110.i04>.

This is a collection of R games and other funny stuff, such as the classic Mine sweeper and sliding puzzles.

The FastPCS algorithm of Vakili and Schmitt (2014) <doi:10.1016/j.csda.2013.07.021> for robust estimation of multivariate location and scatter and multivariate outliers detection.

Create datasets with factorial structure through simulation by specifying variable parameters. Extended documentation at <https://scienceverse.github.io/faux/>. Described in DeBruine (2020) <doi:10.5281/zenodo.2669586>.

Full Consistency Method (FUCOM) for multi-criteria decision-making (MCDM), developed by Dragam Pamucar in 2018 (<doi:10.3390/sym10090393>). The goal of the method is to determine the weights of criteria such that the deviation from full consistency is minimized. Users provide a character vector specifying the ranking of each criterion according to its significance, starting from the criterion expected to have the highest weight to the least significant one. Additionally, users provide a numeric vector specifying the priority values for each criterion. The comparison is made with respect to the first-ranked (most significant) criterion. The function returns the optimized weights for each criterion (summing to 1), the comparative priority (Phi) values, the mathematical transitivity condition (w) value, and the minimum deviation from full consistency (DFC).

Helps you imagine your data before you collect it. Hierarchical data structures and correlated data can be easily simulated, either from random number generators or by resampling from existing data sources. This package is faster with data.table and mvnfast installed.

Utilities to read and write files in the FITS (Flexible Image Transport System) format, a standard format in astronomy (see e.g. <https://en.wikipedia.org/wiki/FITS> for more information). Present low-level routines allow: reading, parsing, and modifying FITS headers; reading FITS images (multi-dimensional arrays); reading FITS binary and ASCII tables; and writing FITS images (multi-dimensional arrays). Higher-level functions allow: reading files composed of one or more headers and a single (perhaps multidimensional) image or single table; reading tables into data frames; generating vectors for image array axes; scaling and writing images as 16-bit integers. Known incompletenesses are reading random group extensions, as well as complex and array descriptor data types in binary tables.

This package provides tools for training and analysing fairness-aware gated neural networks for subgroup-aware prediction and interpretation in clinical datasets. Methods draw on prior work in mixture-of-experts neural networks by Jordan and Jacobs (1994) <doi:10.1007/978-1-4471-2097-1_113>, fairness-aware learning by Hardt, Price, and Srebro (2016) <doi:10.48550/arXiv.1610.02413>, and personalised treatment prediction for depression by Iniesta, Stahl, and McGuffin (2016) <doi:10.1016/j.jpsychires.2016.03.016>.

It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) <arXiv:1505.05660>.

To help you access, transform, analyze, and visualize ForestGEO data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to install and load the entire package-collection with a single R command, and provides convenient ways to find relevant documentation. Most commonly, you should not worry about the individual packages that make up the package-collection as you can access all features via this package. To learn more about ForestGEO visit <http://www.forestgeo.si.edu/>.

The fastai <https://docs.fast.ai/index.html> library simplifies training fast and accurate neural networks using modern best practices. It is based on research in to deep learning best practices undertaken at fast.ai', including out of the box support for vision, text, tabular, audio, time series, and collaborative filtering models.

This package contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).

Fair machine learning regression models which take sensitive attributes into account in model estimation. Currently implementing Komiyama et al. (2018) <http://proceedings.mlr.press/v80/komiyama18a/komiyama18a.pdf>, Zafar et al. (2019) <https://www.jmlr.org/papers/volume20/18-262/18-262.pdf> and my own approach from Scutari, Panero and Proissl (2022) <doi:10.1007/s11222-022-10143-w> that uses ridge regression to enforce fairness.

Extend shiny.semantic with extra Fomantic UI components. Create pages in a format similar to shiny', form validation and more.

Allows the user to create a countdown in RMarkdown documents and shiny applications. The package is a wrapper of the JavaScript library flipdown.js'. See <https://pbutcher.uk/flipdown/> for more info.

Parse and create Darwin Core (<http://rs.tdwg.org/dwc/>) Simple and Archives. Functionality includes reading and parsing all the files in a Darwin Core Archive, including the datasets and metadata; read and parse simple Darwin Core files; and validation of Darwin Core Archives.