This package provides a simple method to select the best model or best subset of variables using different types of data (binary, Gaussian or Poisson) and applying it in different contexts (parametric or non-parametric).

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

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>.

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.

Retrieves financial data from Federal Deposit Insurance Corporation (FDIC)-insured institutions and provides access to the FDIC data taxonomy.

Implementation of dynamic principal component analysis (DPCA), simulation of VAR and VMA processes and frequency domain tools. These frequency domain methods for dimensionality reduction of multivariate time series were introduced by David Brillinger in his book Time Series (1974). We follow implementation guidelines as described in Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.

Finite element modeling of beam structures and 2D geometries using constant strain triangles. Applies material properties and boundary conditions (load and constraint) to generate a finite element model. The model produces stress, strain, and nodal displacements; a heat map is available to demonstrate regions where output variables are high or low. Also provides options for creating a triangular mesh of 2D geometries. Package developed with reference to: Bathe, K. J. (1996). Finite Element Procedures.[ISBN 978-0-9790049-5-7] -- Seshu, P. (2012). Textbook of Finite Element Analysis. [ISBN-978-81-203-2315-5] -- Mustapha, K. B. (2018). Finite Element Computations in Mechanics with R. [ISBN 9781315144474].

This package provides an interface to the Flickr API <https://www.flickr.com/services/api/> and allows R users to download data on Flickr.

Creation of an input model (fitted distribution) via the frequentist model averaging (FMA) approach and generate random-variates from the distribution specified by "myfit" which is the fitted input model via the FMA approach. See W. X. Jiang and B. L. Nelson (2018), "Better Input Modeling via Model Averaging," Proceedings of the 2018 Winter Simulation Conference, IEEE Press, 1575-1586.

This package provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation and Laplace Approximation as developed in Silverman et. Al. (2022) <https://www.jmlr.org/papers/v23/19-882.html>. Key functionality is implemented in C++ for scalability. fido replaces the previous package stray'.

This package provides a simple way to unload none-base packages and remove all global variables.

Implementation of fused Markov graphical model (FMGM; Park and Won, 2022). The functions include building mixed graphical model (MGM) objects from data, inference of networks using FMGM, stable edge-specific penalty selection (StEPS) for the determination of penalization parameters, and the visualization. For details, please refer to Park and Won (2022) <doi:10.48550/arXiv.2208.14959>.

Data-driven fMRI denoising with projection scrubbing (Pham et al (2022) <doi:10.1016/j.neuroimage.2023.119972>). Also includes routines for DVARS (Derivatives VARianceS) (Afyouni and Nichols (2018) <doi:10.1016/j.neuroimage.2017.12.098>), motion scrubbing (Power et al (2012) <doi:10.1016/j.neuroimage.2011.10.018>), aCompCor (anatomical Components Correction) (Muschelli et al (2014) <doi:10.1016/j.neuroimage.2014.03.028>), detrending, and nuisance regression. Projection scrubbing is also applicable to other outlier detection tasks involving high-dimensional data.

The goal of forstringr is to enable complex string manipulation in R especially to those more familiar with LEFT(), RIGHT(), and MID() functions in Microsoft Excel. The package combines the power of stringr with other manipulation packages such as dplyr and tidyr'.

This package provides a dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles. The fpop package is a wrapper to a C++ implementation of the fpop (Functional Pruning Optimal Partioning) algorithm described in Maidstone et al. 2017 <doi:10.1007/s11222-016-9636-3>. The problem of detecting changepoints in an univariate sequence is formulated in terms of minimising the mean squared error over segmentations. The fpop algorithm exactly minimizes the mean squared error for a penalty linear in the number of changepoints.

Simple key-value database using SQLite as the backend.

Brings a set of tools to help and automatically realise the description of principal component analyses (from FactoMineR functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).

This package provides a high-performance framework for deriving bioclimatic and custom summary variables from large-scale climate raster data. The package features a dual-backend architecture that intelligently switches between fast in-memory processing for smaller datasets (via the terra package) and a memory-safe tiled approach for massive datasets that do not fit in RAM (via exactextractr and Rfast'). The main functions, derive_bioclim() and derive_statistics()', offer a unified interface with advanced options for custom time periods and static indices, making it suitable for a wide range of ecological and environmental modeling applications. A software note is in preparation. In the meantime, you can visit the package website <https://gepinillab.github.io/fastbioclim/> to find tutorials in English and Spanish.

Samples generalized random product graphs, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries using the fastRG algorithm of Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17-128.html>. The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes.

This package provides tools, helpers and data structures for developing models and time series functions for fable and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.

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

Given vectors of family sizes and number of affecteds per family, calculates the risk of disease recurrence in an unaffected person, conditional on a family having at least k affected members. Methods also model heterogeneity of disease risk across families by fitting a mixture model, allowing for high and low risk families.

Quantify variability (such as confidence interval) of fertilizer response curves and optimum fertilizer rates using bootstrapping residuals with several popular non-linear and linear models.

Reads cell contents plus formatting from a spreadsheet file and creates an editable gt object with the same data and formatting. Supports the most commonly-used cell and text styles including colors, fills, font weights and decorations, and borders.