Implement and fit a variety of short-memory (SM) and long-memory (LM) models from a very broad family of exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models, such as a MEGARCH (modified EGARCH), FIEGARCH (fractionally integrated EGARCH), FIMLog-GARCH (fractionally integrated modulus Log-GARCH), and more. The FIMLog-GARCH as part of the EGARCH family is discussed in Feng et al. (2023) <https://econpapers.repec.org/paper/pdnciepap/156.htm>. For convenience and the purpose of comparison, a variety of other popular SM and LM GARCH-type models, like an APARCH model, a fractionally integrated APARCH (FIAPARCH) model, standard GARCH and fractionally integrated GARCH (FIGARCH) models, GJR-GARCH and FIGJR-GARCH models, TGARCH and FITGARCH models, are implemented as well as dual models with simultaneous modelling of the mean, including dual long-memory models with a fractionally integrated autoregressive moving average (FARIMA) model in the mean and a long-memory model in the variance, and semiparametric volatility model extensions. Parametric models and parametric model parts are fitted through quasi-maximum-likelihood estimation. Furthermore, common forecasting and backtesting functions for value-at-risk (VaR) and expected shortfall (ES) based on the package's models are provided.

This package provides a set of helper functions for constructing file paths relative to the root of various types of projects, such as R packages, Git repositories, and more. File paths are specified with function arguments, or `$` to navigate into folders to specific files supported by auto-completion.

This package provides a simple and efficient wrapper around the fastest Fourier transform in the west (FFTW) library <http://www.fftw.org/>.

The FAS package implements the bootstrap method for the tuning parameter selection and tuning-free inference on sparse regression coefficient vectors. Currently, the test could be applied to linear and factor-augmented sparse regressions, see Lederer & Vogt (2021, JMLR) <https://www.jmlr.org/papers/volume22/20-539/20-539.pdf> and Beyhum & Striaukas (2023) <arXiv:2307.13364>.

Calculation of Evapotranspiration by FAO Penman-Monteith equation based on Allen, R. G., Pereira, L. S., Raes, D., Smith, M. (1998, ISBN:92-5-104219-5) "Crop evapotranspiration - Guidelines for computing crop water requirements - FAO Irrigation and drainage paper 56".

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.

Adds flow maps to ggplot2 plots. The flow maps consist of ggplot2 layers which visualize the nodes as circles and the bilateral flows between the nodes as bidirectional half-arrows.

This package provides a financial calculator that provides very fast implementations of common financial indicators using Rust code. It includes functions for bond-related indicators, such as yield to maturity ('YTM'), modified duration, and Macaulay duration, as well as functions for calculating time-weighted and money-weighted rates of return (using Modified Dietz method) for multiple portfolios, given their market values and profit and loss ('PnL') data. fcl is designed to be efficient and accurate for financial analysis and computation. The methods used in this package are based on the following references: <https://en.wikipedia.org/wiki/Modified_Dietz_method>, <https://en.wikipedia.org/wiki/Time-weighted_return>.

This package provides a computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software SKAT and coxKM'. coxKM can be obtained from <https://github.com/lin-lab/coxKM>.

This package provides an efficient C++ code for computing an optimal segmentation model with Poisson loss, up-down constraints, and label constraints, as described by Kaufman et al. (2024) <doi:10.1080/10618600.2023.2293216>.

This package provides a tool to use a principal component analysis on radially averaged two dimensional Fourier spectra to characterize image texture. The method within the context of ecology was first described by Couteron et al. (2005) <doi:10.1111/j.1365-2664.2005.01097.x> and expanded upon by Solorzano et al. (2018) <doi:10.1117/1.JRS.12.036006> using a moving window approach.

This package provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.

In the Cramérâ Lundberg risk process perturbed by a Wiener process, this packages provides approximations to the probability of ruin within a finite time horizon. Currently, there are three methods implemented: The first one uses saddlepoint approximation (two variants are provided), the second one uses importance sampling and the third one is based on the simulation of a dual process. This last method is not very accurate and only given here for completeness.

This package implements a very fast C++ algorithm to quickly bootstrap receiver operating characteristics (ROC) curves and derived performance metrics, including the area under the curve (AUC) and the partial area under the curve as well as the true and false positive rate. The analysis of paired receiver operating curves is supported as well, so that a comparison of two predictors is possible. You can also plot the results and calculate confidence intervals. On a typical desktop computer the time needed for the calculation of 100000 bootstrap replicates given 500 observations requires time on the order of magnitude of one second.

Social Relations Analysis with roles ("Family SRM") are computed, using a structural equation modeling approach. Groups ranging from three members up to an unlimited number of members are supported and the mean structure can be computed. Means and variances can be compared between different groups of families and between roles.

Wrapper for computing parameters for univariate distributions using MLE. It creates an object that stores d, p, q, r functions as well as parameters and statistics for diagnostics. Currently supports automated fitting from base and actuar packages. A manually fitting distribution fitting function is included to support directly specifying parameters for any distribution from ancillary packages.

This package performs alignment, PCA, and modeling of multidimensional and unidimensional functions using the square-root velocity framework (Srivastava et al., 2011 <doi:10.48550/arXiv.1103.3817> and Tucker et al., 2014 <DOI:10.1016/j.csda.2012.12.001>). This framework allows for elastic analysis of functional data through phase and amplitude separation.

Some functions of ade4 and stats are combined in order to obtain a partition of the rows of a data table, with columns representing variables of scales: quantitative, qualitative or frequency. First, a principal axes method is performed and then, a combination of Ward agglomerative hierarchical classification and K-means is performed, using some of the first coordinates obtained from the previous principal axes method. In order to permit different weights of the elements to be clustered, the function kmeansW', programmed in C++, is included. It is a modification of kmeans'. Some graphical functions include the option: gg=FALSE'. When gg=TRUE', they use the ggplot2 and ggrepel packages to avoid the super-position of the labels.

An interactive shiny'-based tool for exploration and quality assurance and quality control (QA/QC) of eddy covariance flux tower data processing. It generates data-point removal code via user-directed selection from a scatterplot, and can export a cleaned .csv with removed points set to NA plus an R script for reproducibility. Reference: Key (2025) <DOI:10.5281/zenodo.15597159>.

This package implements a novel approach for measuring feature importance in k-means clustering. Importance of a feature is measured by the misclassification rate relative to the baseline cluster assignment due to a random permutation of feature values. An explanation of permutation feature importance in general can be found here: <https://christophm.github.io/interpretable-ml-book/feature-importance.html>.

Edit vectors to fill missing values, based on the vector itself.

This package provides a collection of toys to do things like generate Collatz and other interesting sequences, calculate a fraction which is a close approximation to some value (e.g., 22/7 or 355/113 for pi), and so on.

This package provides methods for computing and visualizing wildfire ignition exposure and directional vulnerability that are published in a series of scientific publications are automated by the functions in this package. See Beverly et al. (2010) <doi:10.1071/WF09071>, Beverly et al. (2021) <doi:10.1007/s10980-020-01173-8>, and Beverly and Forbes (2023) <doi:10.1007/s11069-023-05885-3> for background and methodology.

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.