This package provides tools to perform fuzzy formal concept analysis, presented in Wille (1982) <doi:10.1007/978-3-642-01815-2_23> and in Ganter and Obiedkov (2016) <doi:10.1007/978-3-662-49291-8>. It provides functions to load and save a formal context, extract its concept lattice and implications. In addition, one can use the implications to compute semantic closures of fuzzy sets and, thus, build recommendation systems. Matrix factorization is provided by the GreConD+ algorithm (Belohlavek and Trneckova, 2024 <doi:10.1109/TFUZZ.2023.3330760>).

This package provides a small set of tools for formatting numbers in R-markdown documents. Convert a numerical vector to character strings in power-of-ten form, decimal form, or measurement-units form; all are math-delimited for rendering as inline equations. Can also convert text into math-delimited text to match the font face and size of math-delimited numbers. Useful for rendering single numbers in inline R code chunks and for rendering columns in tables.

This package provides tools for downloading and analyzing floristic quality assessment data. See Freyman et al. (2015) <doi:10.1111/2041-210X.12491> for more information about floristic quality assessment and the associated database.

Fit data to an ellipse, hyperbola, or parabola. Bootstrapping is available when needed. The conic curve can be rotated through an arbitrary angle and the fit will still succeed. Helper functions are provided to convert generator coefficients from one style to another, generate test data sets, rotate conic section parameters, and so on. References include Nikolai Chernov (2014) "Fitting ellipses, circles, and lines by least squares" <https://people.cas.uab.edu/~mosya/cl/>; A. W. Fitzgibbon, M. Pilu, R. B. Fisher (1999) "Direct Least Squares Fitting of Ellipses" IEEE Trans. PAMI, Vol. 21, pages 476-48; N. Chernov, Q. Huang, and H. Ma (2014) "Fitting quadratic curves to data points", British Journal of Mathematics & Computer Science, 4, 33-60; N. Chernov and H. Ma (2011) "Least squares fitting of quadratic curves and surfaces", Computer Vision, Editor S. R. Yoshida, Nova Science Publishers, pp. 285-302.

The classical (i.e. Efron's, see Efron and Tibshirani (1994, ISBN:978-0412042317) "An Introduction to the Bootstrap") bootstrap is widely used for both the real (i.e. "crisp") and fuzzy data. The main aim of the algorithms implemented in this package is to overcome a problem with repetition of a few distinct values and to create fuzzy numbers, which are "similar" (but not the same) to values from the initial sample. To do this, different characteristics of triangular/trapezoidal numbers are kept (like the value, the ambiguity, etc., see Grzegorzewski et al. <doi:10.2991/eusflat-19.2019.68>, Grzegorzewski et al. (2020) <doi:10.2991/ijcis.d.201012.003>, Grzegorzewski et al. (2020) <doi:10.34768/amcs-2020-0022>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-030-95929-6_3>, Romaniuk and Hryniewicz (2019) <doi:10.1007/s00500-018-3251-5>). Some additional procedures related to these resampling methods are also provided, like calculation of the Bertoluzza et al.'s distance (aka the mid/spread distance, see Bertoluzza et al. (1995) "On a new class of distances between fuzzy numbers") and estimation of the p-value of the one- and two- sample bootstrapped test for the mean (see Lubiano et al. (2016, <doi:10.1016/j.ejor.2015.11.016>)). Additionally, there are procedures which randomly generate trapezoidal fuzzy numbers using some well-known statistical distributions.

Useful functions to standardize software outputs from ProteomeDiscoverer, Spectronaut, DIA-NN and MaxQuant on precursor, modified peptide and proteingroup level and to trace software differences for identifications such as varying proteingroup denotations for common precursor.

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.

This package provides a wrapper for the Filebin API. Filebin implements convenient file sharing on the web.

This package provides a "tabular-data-resource" (<https://specs.frictionlessdata.io/tabular-data-resource/>) is a simple format to describe a singular tabular data resource such as a CSV file. It includes support both for metadata such as author and title and a schema to describe the data, for example the types of the fields/columns in the data. Create a tabular-data-resource by providing a data.frame and specifying metadata. Write and read tabular-data-resources to and from disk.

Processing of large-in-memory/large-on disk rasters and spatial vectors using GRASS <https://grass.osgeo.org/>. Most functions in the terra package are recreated. Processing of medium-sized and smaller spatial objects will nearly always be faster using terra or sf', but for large-in-memory/large-on-disk objects, fasterRaster may be faster. To use most of the functions, you must have the stand-alone version (not the OSGeoW4 installer version) of GRASS 8.0 or higher.

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> .

Perform various floating catchment area methods to calculate a spatial accessibility index (SPAI) for demand point data. The distance matrix used for weighting is normalized in a preprocessing step using common functions (gaussian, gravity, exponential or logistic).

Use R to access to the FMP Cloud API <https://fmpcloud.io/> and Financial Modeling Prep API <https://financialmodelingprep.com/developer/docs/>. Data available includes stock prices, market indexes, company fundamentals, 13F holdings data, and much more. A valid API token must be set to enable functions.

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.

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>.

Perform optimal transport based tests in factorial designs as introduced in Groppe et al. (2025) <doi:10.48550/arXiv.2509.13970> via the FDOTT() function. These tests are inspired by ANOVA and its nonparametric counterparts. They allow for testing linear relationships in factorial designs between finitely supported probability measures on a metric space. Such relationships include equality of all measures (no treatment effect), interaction effects between a number of factors, as well as main and simple factor effects.

This package provides a joint model for large-scale, competing risks time-to-event data with singular or multiple longitudinal biomarkers, implemented with the efficient algorithms developed by Li and colleagues (2022) <doi:10.1155/2022/1362913> and <doi:10.48550/arXiv.2506.12741>. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-fixed covariates. The longitudinal biomarkers are modelled using a linear mixed effects model. The association between the longitudinal submodel and the survival submodel is captured through shared random effects. It allows researchers to analyze large-scale data to model biomarker trajectories, estimate their effects on event outcomes, and dynamically predict future events from patientsâ past histories. A function for simulating survival and longitudinal data for multiple biomarkers is also included alongside built-in datasets.

Three methods are implemented in R to facilitate the aggregations of flags in official statistics. From the underlying flags the highest in the hierarchy, the most frequent, or with the highest total weight is propagated to the flag(s) for EU or other aggregates. Below there are some reference documents for the topic: <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_v2_1.docx>, <https://sdmx.org/wp-content/uploads/CL_CONF_STATUS_1_2_2018.docx>, <http://ec.europa.eu/eurostat/data/database/information>, <http://www.oecd.org/sdd/33869551.pdf>, <https://sdmx.org/wp-content/uploads/CL_OBS_STATUS_implementation_20-10-2014.pdf>.

Implementation of the Stochastic Expectation Maximisation (StEM) approach to Record Linkage described in the paper by K. Robach, S. L. van der Pas, M. A. van de Wiel and M. H. Hof (2024, <doi:10.1093/jrsssc/qlaf016>); see citation("FlexRL") for details. This is a record linkage method, for finding the common set of records among 2 data sources based on Partially Identifying Variables (PIVs) available in both sources. It includes modelling of dynamic Partially Identifying Variables (e.g. postal code) that may evolve over time and registration errors (missing values and mistakes in the registration). Low memory footprint.

Stores large arrays in files to avoid occupying large memories. Implemented with super fast gigabyte-level multi-threaded reading/writing via OpenMP'. Supports multiple non-character data types (double, float, complex, integer, logical, and raw).

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/>.

Fast censored linear regression for the accelerated failure time (AFT) model of Huang (2013) <doi:10.1111/sjos.12031>.

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 package provides a game for two players: Who gets first four in a row (horizontal, vertical or diagonal) wins. As board game published by Milton Bradley, designed by Howard Wexler and Ned Strongin.