Integrate Item Response Theory (IRT) and Federated Learning to estimate traditional IRT models, including the 2-Parameter Logistic (2PL) and the Graded Response Models, with enhanced privacy. It allows for the estimation in a distributed manner without compromising accuracy. A user-friendly shiny application is included.

This package provides tools to work with the Flexible Dirichlet distribution. The main features are an E-M algorithm for computing the maximum likelihood estimate of the parameter vector and a function based on conditional bootstrap to estimate its asymptotic variance-covariance matrix. It contains also functions to plot graphs, to generate random observations and to handle compositional data.

Probabilistic distance clustering (PD-clustering) is an iterative, distribution-free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership under the constraint that the product of the probability and the distance of each point to any cluster center is a constant. PD-clustering is a flexible method that can be used with elliptical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different sizes. GPDC and TPDC use a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high-dimensional data sets.

This package provides a bundle of analytics tools for fisheries scientists. A shiny R App is included for a no-code solution for retrieval, analysis, and visualization.

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.

Likelihood based analysis of 1-dimension functional data in a mixed-effects model framework. Matrix computation are approximated by semi-explicit operator equivalents with linear computational complexity. Markussen (2013) <doi:10.3150/11-BEJ389>.

Read and write PNG images with arrays, rasters, native rasters, numeric arrays, integer arrays, raw vectors and indexed values. This PNG encoder exposes configurable internal options enabling the user to select a speed-size tradeoff. For example, disabling compression can speed up writing PNG by a factor of 50. Multiple image formats are supported including raster, native rasters, and integer and numeric arrays at color depths of 1, 2, 3 or 4. 16-bit images are also supported. This implementation uses the libspng C library which is available from <https://github.com/randy408/libspng/>.

This package provides an alternative to facilitate the construction of a phylogeny for fish species from a list of species or a community matrix using as a backbone the phylogenetic tree proposed by Rabosky et al. (2018) <doi:10.1038/s41586-018-0273-1>.

Implementation of the fast univariate inference approach (Cui et al. (2022) <doi:10.1080/10618600.2021.1950006>, Loewinger et al. (2024) <doi:10.7554/eLife.95802.2>, Xin et al. (2025)) for fitting functional mixed models. User guides and Python package information can be found at <https://github.com/gloewing/photometry_FLMM>.

This package provides a collection of functions to optimize portfolios and to analyze them from different points of view.

We provide a framework for rendering complex tables to ASCII, and a set of formatters for transforming values or sets of values into ASCII-ready display strings.

We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model. The package is an implementation of Yang, Y. and Zou, H. (2013) <doi:10.4310/SII.2013.v6.n2.a1>.

Easily create graphs of the inter-relationships between functions in an environment.

This package provides functions for sequencing studies allowing for multiple functional annotation scores. Score type tests and an efficient perturbation method are used for individual gene/large gene-set/genome wide analysis. Only summary statistics are needed.

This package provides a shiny application based on FossilSim'. Used for simulating tree, taxonomic and fossil data under mechanistic models of speciation, preservation and sampling.

Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes.

This package provides a set of simplified functions for creating funnel plots for proportion data. This package supports user defined benchmarks, confidence limits and estimation methods (i.e. exact or approximate) based on Spiegelhalter (2005) <doi:10.1002/sim.1970>. Additional routines for returning scored unit level data according to a set of specifications is also implemented for convenience. Specifically, both a categorical and a continuous score variable is returned to the sample data frame, which identifies which observations are deemed extreme or in control. Typically, such variables are useful as stratifications or covariates in further exploratory analyses. Lastly, the plotting routine returns a base funnel plot ('ggplot2'), which can also be tailored.

This package provides functionality for clustering origin-destination (OD) pairs, representing desire lines (or flows). This includes creating distance matrices between OD pairs and passing distance matrices to a clustering algorithm. See the academic paper Tao and Thill (2016) <doi:10.1111/gean.12100> for more details on spatial clustering of flows. See the paper on delineating demand-responsive operating areas by Mahfouz et al. (2025) <doi:10.1016/j.urbmob.2025.100135> for an example of how this package can be used to cluster flows for applied transportation research.

This package provides a toolkit for Flux Balance Analysis and related metabolic modeling techniques. Functions are provided for: parsing models in tabular format, converting parsed metabolic models to input formats for common linear programming solvers, and evaluating and applying gene-protein-reaction mappings. In addition, there are wrappers to parse a model, select a solver, find the metabolic fluxes, and return the results applied to the original model. Compared to other packages in this field, this package puts a much heavier focus on providing reusable components that can be used in the design of new implementation of new techniques, in particular those that involve large parameter sweeps. For a background on the theory, see What is Flux Balance Analysis <doi:10.1038/nbt.1614>.

This package provides a web application for displaying, analysing and forecasting univariate time series. Includes basic methods such as mean, naïve, seasonal naïve and drift, as well as more complex methods such as Holt-Winters Box,G and Jenkins, G (1976) <doi:10.1111/jtsa.12194> and ARIMA Brockwell, P.J. and R.A.Davis (1991) <doi:10.1007/978-1-4419-0320-4>.

Estimation of mixed models including a subject-specific variance which can be time and covariate dependent. In the joint model framework, the package handles left truncation and allows a flexible dependence structure between the competing events and the longitudinal marker. The estimation is performed under the frequentist framework, using the Marquardt-Levenberg algorithm. (Courcoul, Tzourio, Woodward, Barbieri, Jacqmin-Gadda (2023) <arXiv:2306.16785>).

This package provides tools to quickly compile taxonomic and distribution data from the Brazilian Flora 2020.

Allows users to create and deploy the workflow with multiple functions in Function-as-a-Service (FaaS) cloud computing platforms. The FaaSr package makes it simpler for R developers to use FaaS platforms by providing the following functionality: 1) Parsing and validating a JSON-based payload compliant to FaaSr schema supporting multiple FaaS platforms 2) Invoking user functions written in R in a Docker container (derived from rocker), using a list generated from the parser as argument 3) Downloading/uploading of files from/to S3 buckets using simple primitives 4) Logging to files in S3 buckets 5) Triggering downstream actions supporting multiple FaaS platforms 6) Generating FaaS-specific API calls to simplify the registering of a user's workflow with a FaaS platform Supported FaaS platforms: Apache OpenWhisk <https://openwhisk.apache.org/> GitHub Actions <https://github.com/features/actions> Amazon Web Services (AWS) Lambda <https://aws.amazon.com/lambda/> Supported cloud data storage for persistent storage: Amazon Web Services (AWS) Simple Storage Service (S3) <https://aws.amazon.com/s3/>.

Set of tools for detecting and analyzing Airborne Laser Scanning-derived Tropical Forest Canopy Gaps. Details were published in Silva and others (2019) <doi:10.1111/2041-210X.13211>.