Design and simulate fuzzy logic systems using Type-1 and Interval Type-2 Fuzzy Logic. This toolkit includes with graphical user interface (GUI) and an adaptive neuro- fuzzy inference system (ANFIS). This toolkit is a continuation from the previous package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis Group (IMA) and Lab for UnCertainty In Data and decision making (LUCID), University of Nottingham. A big thank you to the many people who have contributed to the development/evaluation of the toolbox. Please cite the toolbox and the corresponding paper <doi:10.1109/FUZZ48607.2020.9177780> when using it. More related papers can be found in the NEWS.

Has two functions to help with calculating feature selection stability. Lump is a function that groups subset vectors into a dataframe, and adds NA to shorter vectors so they all have the same length. ASM is a function that takes a dataframe of subset vectors and the original vector of features as inputs, and calculates the Stability of the feature selection. The calculation for asm uses the Adjusted Stability Measure proposed in: Lustgarten', Gopalakrishnan', & Visweswaran (2009)<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815476/>.

Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results.

This package provides tools to analyze R source code and detect function definitions and their internal dependencies across multiple files. Creates interactive network visualizations using visNetwork to display function call relationships, with detailed tooltips showing function arguments, return values, and documentation. Supports both individual files and directory-based analysis with automatic file detection. Useful for understanding code structure, identifying dependencies, and documenting R projects.

This package provides functions for creating flashcard decks of terms and definitions. This package creates HTML slides using revealjs that can be viewed in the RStudio viewer or a web browser. Users can create flashcards from either existing built-in decks or create their own from CSV files or vectors of function names.

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

This package provides a flexible set of tools for matching two un-linked data sets. fedmatch allows for three ways to match data: exact matches, fuzzy matches, and multi-variable matches. It also allows an easy combination of these three matches via the tier matching function.

The funHDDC algorithm allows to cluster functional univariate (Bouveyron and Jacques, 2011, <doi:10.1007/s11634-011-0095-6>) or multivariate data (Schmutz et al., 2018) by modeling each group within a specific functional subspace.

This package provides analytics directly from R'. It requires: FormShare App': <https://github.com/qlands/FormShare >= 2.22.0> . Analytics plugin: <https://github.com/qlands/formshare_analytics_plugin> . Remote SQL plugin: <https://github.com/qlands/formshare_sql_plugin> .

This package provides a program to generate smoothed quantiles for the Fst-heterozygosity distribution. Designed for use with large numbers of loci (e.g., genome-wide SNPs). The best case for analyzing the Fst-heterozygosity distribution is when many populations (>10) have been sampled. See Flanagan & Jones (2017) <doi:10.1093/jhered/esx048>.

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.

This package creates participant flow diagrams directly from a dataframe. Representing the flow of participants through each stage of a study, especially in clinical trials, is essential to assess the generalisability and validity of the results. This package provides a set of functions that can be combined with a pipe operator to create all kinds of flowcharts from a data frame in an easy way.

This package provides a collection of functions that would help one to build features based on external data. Very useful for Data Scientists in data to day work. Many functions create features using parallel computation. Since the nitty gritty of parallel computation is hidden under the hood, the user need not worry about creating clusters and shutting them down.

This package provides four addons for analyzing trends and unit roots in financial time series: (i) functions for the density and probability of the augmented Dickey-Fuller Test, (ii) functions for the density and probability of MacKinnon's unit root test statistics, (iii) reimplementations for the ADF and MacKinnon Test, and (iv) an urca Unit Root Test Interface for Pfaff's unit root test suite.

This package implements the formulae required to calculate freedom from disease according to Cameron and Baldock (1998) <doi:10.1016/S0167-5877(97)00081-0>. These are the methods used at the Swedish national veterinary institute (SVA) to evaluate the performance of our nation animal disease surveillance programmes.

Calculate useful quantities for a user-defined differential equation model of infectious disease transmission among individuals in a healthcare facility. Input rates of transition between states of individuals with and without the disease-causing organism, distributions of states at facility admission, relative infectivity of transmissible states, and the facility length of stay distribution. Calculate the model equilibrium and the basic facility reproduction number, as described in Toth et al. (2025) <doi:10.1371/journal.pcbi.1013577>.

This package provides very fast logistic regression with coefficient inferences plus other useful methods such as a forward stepwise model generator (see the benchmarks by visiting the github page at the URL below). The inputs are flexible enough to accomodate GPU computations. The coefficient estimation employs the fastLR() method in the RcppNumerical package by Yixuan Qiu et al. This package allows their work to be more useful to a wider community that consumes inference.

This is a package for implementation of Flury-Gautschi algorithms.

Feature Ordering by Integrated R square Dependence (FORD) is a variable selection algorithm based on the new measure of dependence: Integrated R2 Dependence Coefficient (IRDC). For more information, see the paper: Azadkia and Roudaki (2025),"A New Measure Of Dependence: Integrated R2" <doi:10.48550/arXiv.2505.18146>.

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/>.

This package provides support for building Feldman-Cousins confidence intervals [G. J. Feldman and R. D. Cousins (1998) <doi:10.1103/PhysRevD.57.3873>].

Weighted-L2 FPOP Maidstone et al. (2017) <doi:10.1007/s11222-016-9636-3> and pDPA/FPSN Rigaill (2010) <arXiv:1004.0887> algorithm for detecting multiple changepoints in the mean of a vector. Also includes a few model selection functions using Lebarbier (2005) <doi:10.1016/j.sigpro.2004.11.012> and the capsushe package.

SHE, FORAM Index and ABC Method analyses and custom plot functions for community data.

This package provides functions for printing the contents of a folder as columns in a ragged-bottom data.frame and for viewing the details (size, time created, time modified, etc.) of a folder's top level contents.