This package provides functions for plotting probability density functions, distribution functions, survival functions, hazard functions and computing distribution moments. The implementation is inspired by Delignette-Muller and Dutang (2015) <doi:10.18637/jss.v064.i04>.

This package contains functions to fetch data from various data sources. The user first creates a catalog of objects from a data source, then fetches data from the catalog. The package provides an easy way to access data from many different types of sources.

Statistical tool set for population genetics. The package provides following functions: 1) estimators of genetic differentiation (FST), 2) regression analysis of environmental effects on genetic differentiation using generalized least squares (GLS) method, 3) interfaces to read and manipulate GENEPOP format data files). For more information, see Kitada, Nakamichi and Kishino (2020) <doi:10.1101/2020.01.30.927186>.

Provide a range of plugins for fiery web servers that handle different aspects of server-side web security. Be aware that security cannot be handled blindly, and even though these plugins will raise the security of your server you should not build critical infrastructure without the aid of a security expert.

With no external dependencies and support for 335 languages; all languages spoken by more than one million speakers. Franc is a port of the JavaScript project of the same name, see <https://github.com/wooorm/franc>.

Estimation and inference using the Fractionally Cointegrated Vector Autoregressive (VAR) model. It includes functions for model specification, including lag selection and cointegration rank selection, as well as a comprehensive set of options for hypothesis testing, including tests of hypotheses on the cointegrating relations, the adjustment coefficients and the fractional differencing parameters. An article describing the FCVAR model with examples is available on the Webpage <https://sites.google.com/view/mortennielsen/software>.

The Futureverse is a set of packages for parallel and distributed process with the future package at its core, cf. Bengtsson (2021) <doi:10.32614/RJ-2021-048>. This package is designed to make it easy to install common Futureverse packages in a single step. This package is intended for end-users, interactive use, and R scripts. Packages must not list it as a dependency - instead, explicitly declare each Futureverse package as a dependency as needed.

Construction and smart selection of Gaussian process models for analysis of computer experiments with emphasis on treatment of functional inputs that are regularly sampled. This package offers: (i) flexible modeling of functional-input regression problems through the fairly general Gaussian process model; (ii) built-in dimension reduction for functional inputs; (iii) heuristic optimization of the structural parameters of the model (e.g., active inputs, kernel function, type of distance). An in-depth tutorial in the use of funGp is provided in Betancourt et al. (2024) <doi:10.18637/jss.v109.i05> and Metamodeling background is provided in Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>. The algorithm for structural parameter optimization is described in <https://hal.science/hal-02532713>.

Special procedures for the imputation of missing fuzzy numbers are still underdeveloped. The goal of the package is to provide the new d-imputation method (DIMP for short, Romaniuk, M. and Grzegorzewski, P. (2023) "Fuzzy Data Imputation with DIMP and FGAIN" RB/23/2023) and covert some classical ones applied in R packages ('missForest','miceRanger','knn') for use with fuzzy datasets. Additionally, specially tailored benchmarking tests are provided to check and compare these imputation procedures with fuzzy datasets.

The fusion learning method uses a model selection algorithm to learn from multiple data sets across different experimental platforms through group penalization. The responses of interest may include a mix of discrete and continuous variables. The responses may share the same set of predictors, however, the models and parameters differ across different platforms. Integrating information from different data sets can enhance the power of model selection. Package is based on Xin Gao, Raymond J. Carroll (2017) <arXiv:1610.00667v1>.

This package implements the h-likelihood estimation procedures for general frailty models including competing-risk models and joint models.

An easy-to-use web client/wrapper for the Figma API <https://www.figma.com/developers/api>. It allows you to bring all data from a Figma file to your R session. This includes the data of all objects that you have drawn in this file, and their respective canvas/page metadata.

Find functions in an unstructured directory and explore their dependencies. Sourcing of R source files is performed without side-effects: from R scripts that have executable code and function definitions only functions are sourced.

This package provides allele frequency data for Short Tandem Repeat human genetic markers commonly used in forensic genetics for human identification and kinship analysis. Includes published population frequency data from the US National Institute of Standards and Technology, Federal Bureau of Investigation and the UK government.

Specialized solvers for combinatorial optimization problems in the Subset Sum family. The solvers differ from the mainstream in the options of (i) restricting subset size, (ii) bounding subset elements, (iii) mining real-value multisets with predefined subset sum errors, (iv) finding one or more subsets in limited time. A novel algorithm for mining the one-dimensional Subset Sum induced algorithms for the multi-Subset Sum and the multidimensional Subset Sum. The multi-threaded framework for the latter offers exact algorithms to the multidimensional Knapsack and the Generalized Assignment problems. Historical updates include (a) renewed implementation of the multi-Subset Sum, multidimensional Knapsack and Generalized Assignment solvers; (b) availability of bounding solution space in the multidimensional Subset Sum; (c) fundamental data structure and architectural changes for enhanced cache locality and better chance of SIMD vectorization; (d) option of mapping floating-point instance to compressed 64-bit integer instance with user-controlled precision loss, which could yield substantial speedup due to the dimension reduction and efficient compressed integer arithmetic via bit-manipulations; (e) distributed computing infrastructure for multidimensional subset sum; (f) arbitrary-precision zero-margin-of-error multidimensional Subset Sum accelerated by a simplified Bloom filter. The package contains a copy of xxHash from <https://github.com/Cyan4973/xxHash>. Package vignette (<doi:10.48550/arXiv.1612.04484>) detailed a few historical updates. Functions prefixed with aux (auxiliary) are independent implementations of published algorithms for solving optimization problems less relevant to Subset Sum.

Interval estimation of the population allele frequency from qPCR analysis based on the restriction enzyme digestion (RED)-DeltaDeltaCq method (Osakabe et al. 2017, <doi:10.1016/j.pestbp.2017.04.003>), as well as general DeltaDeltaCq analysis. Compatible with the Cq measurement of DNA extracted from multiple individuals at once, so called "group-testing", this model assumes that the quantity of DNA extracted from an individual organism follows a gamma distribution. Therefore, the point estimate is robust regarding the uncertainty of the DNA yield.

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/>.

Extends data.table join functionality, lets it work with any data frame class, and provides a familiar x'/'y'-style interface, enabling broad use across R. Offers NA-safe matching by default, on-the-fly column selection, multiple match-handling on both sides, x or y row order, and a row origin indicator. Performs inner, left, right, full, semi- and anti-joins with equality and inequality conditions, plus cross joins. Specific support for data.table', (grouped) tibble, and sf'/'sfc objects and their attributes; returns a plain data frame otherwise. Avoids data-copying of inputs and outputs. Allows displaying the data.table code instead of (or as well as) executing it.

Estimation of a dynamic lognormal - Generalized Pareto mixture via the Approximate Maximum Likelihood and the Cross-Entropy methods. See Bee, M. (2023) <doi:10.1016/j.csda.2023.107764>.

This package provides a utility to scrape and load play-by-play data and statistics from the Premier Hockey Federation (PHF) <https://www.premierhockeyfederation.com/>, formerly known as the National Women's Hockey League (NWHL). Additionally, allows access to the National Hockey League's stats API <https://www.nhl.com/>.

Fit a fractional binomial regression model and extended zero-inflated negative binomial regression model to count data with excess zeros using maximum likelihood estimation. Compare zero-inflated regression models via Vuong closeness test.

This package provides plugins for setting up fiery apps as a reverse proxy. This allows you to use a fiery server as a front for multiple services or even work as a load-balancer.

Wrapper functions that interface with Freesurfer <https://surfer.nmr.mgh.harvard.edu/>, a powerful and commonly-used neuroimaging software, using system commands. The goal is to be able to interface with Freesurfer completely in R, where you pass R objects of class nifti', implemented by package oro.nifti', and the function executes an Freesurfer command and returns an R object of class nifti or necessary output.

TrainFastImputation() uses training data to describe a multivariate normal distribution that the data approximates or can be transformed into approximating and stores this information as an object of class FastImputationPatterns'. FastImputation() function uses this FastImputationPatterns object to impute (make a good guess at) missing data in a single line or a whole data frame of data. This approximates the process used by Amelia <https://gking.harvard.edu/amelia> but is much faster when filling in values for a single line of data.