Compute energy fluxes in trophic networks, from resources to their consumers, and can be applied to systems ranging from simple two-species interactions to highly complex food webs. It implements the approach described in Gauzens et al. (2017) <doi:10.1101/229450> to calculate energy fluxes, which are also used to calculate equilibrium stability.

This package provides a lightweight package to compute Maximal Overlap Discrete Wavelet Transform (MODWT) and à Trous Discrete Wavelet Transform by leveraging the power of Rcpp to make these operations fast. This package was designed for use in forecasting, and allows users avoid the inclusion of future data when performing wavelet decomposition of time series. See Quilty and Adamowski (2018) <doi:10.1016/j.jhydrol.2018.05.003>.

Small set of functions designed to speed up the computation of certain matrix operations that are commonly used in statistics and econometrics. It provides efficient implementations for the computation of several structured matrices, matrix decompositions and statistical procedures, many of which have minimal memory overhead. Furthermore, the package provides interfaces to C code callable by another C code from other R packages.

Providing classes, methods, and functions to deal with financial networks. Users can easily store information about both physical and legal persons by using pre-made classes that are studied for integration with scraping packages such as rvest and RSelenium'. Moreover, the package assists in creating various types of financial networks depending on the type of relation between its units depending on the relation under scrutiny (ownership, board interlocks, etc.), the desired tie type (valued or binary), and renders them in the most common formats (adjacency matrix, incidence matrix, edge list, igraph', network'). There are also ad-hoc functions for the Fiedler value, global network efficiency, and cascade-failure analysis.

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.

Data and functions for the book "Multivariate Statistical Modelling Based on Generalized Linear Models", first edition, by Ludwig Fahrmeir and Gerhard Tutz. Useful when using the book.

Calculation and plotting of instantaneous unavailabilities of basic events along with the top event of fault trees are issues important in reliability analysis of complex systems. Here, a fault tree is provided in terms of its minimal cut sets, along with reliability and maintainability distribution functions of the basic events. All the methods are derived from Horton (2002, ISBN: 3-936150-21-4), Niloofar and Lazarova-Molnar (2022).

Get spatial vector data from the Atlas du Patrimoine (<http://atlas.patrimoines.culture.fr/atlas/trunk/>), the official national platform of the French Ministry of Culture, and facilitate its use within R geospatial workflows. The package provides functions to list available heritage datasets, query and retrieve heritage data using spatial queries based on user-provided sf objects, perform spatial filtering operations, and return results as sf objects suitable for spatial analysis, mapping, and integration into heritage management and landscape studies.

The fftab package stores Fourier coefficients in a tibble and allows their manipulation in various ways. Functions are available for converting between complex, rectangular ('re', im'), and polar ('mod', arg') representations, as well as for extracting components as vectors or matrices. Inputs can include vectors, time series, and arrays of arbitrary dimensions, which are restored to their original form when inverting the transform. Since fftab stores Fourier frequencies as columns in the tibble, many standard operations on spectral data can be easily performed using tidy packages like dplyr'.

This package provides core computational operations in C++ via RcppArmadillo', enabling faster performance than pure R, improved numerical stability, and parallel execution with OpenMP where available. On systems without OpenMP support, the package automatically falls back to single-threaded execution with no user configuration required. For efficient model selection, it integrates with CVST to provide sequential-testing cross-validation that identifies competitive hyperparameters without exhaustive grid search. The package offers a unified interface for exact kernel ridge regression and three scalable approximationsâ Nyström, Pivoted Cholesky, and Random Fourier Featuresâ allowing analyses with substantially larger sample sizes than are feasible with exact KRR. It also integrates with the tidymodels ecosystem via the parsnip model specification krr_reg', and the S3 method tunable.krr_reg(). To understand the theoretical background, one can refer to Wainwright (2019) <doi:10.1017/9781108627771>.

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.

Computer Modern font with Paul Murrell's symbol extensions. Is is to be used with the **extrafont** package. When this font package is installed, the CM fonts will be available for PDF or Postscript output files; however, this will (probably) not make the font available for screen or bitmap output files.

This package provides a collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542> and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.

Implementation of a simple algorithm designed for online multivariate changepoint detection of a mean in sparse changepoint settings. The algorithm is based on a modified cusum statistic and guarantees control of the type I error on any false discoveries, while featuring O(1) time and O(1) memory updates per series as well as a proven detection delay.

This package provides a wide variety of tools for general data analysis, wrangling, spelling, statistics, visualizations, package development, and more. All functions have vectorized implementations whenever possible. Exported names are designed to be readable, with longer names possessing short aliases.

Random simulations of fuzzy numbers are still a challenging problem. The aim of this package is to provide the respective procedures to simulate fuzzy random variables, especially in the case of the piecewise linear fuzzy numbers (PLFNs, see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for the further details). Additionally, the special resampling algorithms known as the epistemic bootstrap are provided (see Grzegorzewski and Romaniuk (2022) <doi:10.34768/amcs-2022-0021>, Grzegorzewski and Romaniuk (2022) <doi:10.1007/978-3-031-08974-9_39>, Romaniuk et al. (2024) <doi:10.32614/RJ-2024-016>) together with the functions to apply statistical tests and estimate various characteristics based on the epistemic bootstrap. The package also includes real-life datasets of epistemic fuzzy triangular and trapezoidal numbers. The fuzzy numbers used in this package are consistent with the FuzzyNumbers package.

Regular and non-regular Fractional Factorial 2-level designs can be created. Furthermore, analysis tools for Fractional Factorial designs with 2-level factors are offered (main effects and interaction plots for all factors simultaneously, cube plot for looking at the simultaneous effects of three factors, full or half normal plot, alias structure in a more readable format than with the built-in function alias).

Helpers for parsing out the R functions and packages used in R scripts and notebooks.

Download data sets from Kenneth's French finance data library site <http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html>, reads all the data subsets from the file. Allows R users to collect the data as tidyverse'-ready data frames.

Tests for Kaiser-Meyer-Olkin (KMO) and communalities in a dataset. It provides a final sample by removing variables in a iterable manner while keeping account of the variables that were removed in each step. It follows the best practices and assumptions according to Hair, Black, Babin & Anderson (2018, ISBN:9781473756540).

Estimate the of fractal dimension of a black area in 2D and 3D (slices) images using the box-counting method. See Klinkenberg B. (1994) <doi:10.1007/BF02065874>.

Upload, download, and edit internet maps with the Felt API (<https://developers.felt.com/rest-api/getting-started>). Allows users to create new maps, edit existing maps, and extract data. Provides tools for working with layers, which represent geographic data, and elements, which are interactive annotations. Spatial data accessed from the API is transformed to work with sf'.

It implements an improved and computationally faster version of the original Stepwise Gaussian Graphical Algorithm for estimating the Omega precision matrix from high-dimensional data. Zamar, R., Ruiz, M., Lafit, G. and Nogales, J. (2021) <doi:10.52933/jdssv.v1i2.11>.

Computes fungible coefficients and Monte Carlo data. Underlying theory for these functions is described in the following publications: Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI:10.1007/s11336-008-9066-z>. Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights. Psychometrika, 74(4), 589-602, <DOI:10.1007/s11336-008-9087-7>. Waller, N. G. (2016). Fungible Correlation Matrices: A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568. Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF) covariance matrix of standardized regression coefficients: theoretical extensions and finite sample behavior. Psychometrika, 80, 365-378, <DOI:10.1007/s11336-013-9380-y>. Waller, N. G. (2018). Direct Schmid-Leiman transformations and rank-deficient loadings matrices. Psychometrika, 83, 858-870. <DOI:10.1007/s11336-017-9599-0>.