R-universe search: exports:rgev

Showing 14 of total 14 results (show query)

twolodzko

extraDistr:Additional Univariate and Multivariate Distributions

Density, distribution function, quantile function and random generation for a number of univariate and multivariate distributions. This package implements the following distributions: Bernoulli, beta-binomial, beta-negative binomial, beta prime, Bhattacharjee, Birnbaum-Saunders, bivariate normal, bivariate Poisson, categorical, Dirichlet, Dirichlet-multinomial, discrete gamma, discrete Laplace, discrete normal, discrete uniform, discrete Weibull, Frechet, gamma-Poisson, generalized extreme value, Gompertz, generalized Pareto, Gumbel, half-Cauchy, half-normal, half-t, Huber density, inverse chi-squared, inverse-gamma, Kumaraswamy, Laplace, location-scale t, logarithmic, Lomax, multivariate hypergeometric, multinomial, negative hypergeometric, non-standard beta, normal mixture, Poisson mixture, Pareto, power, reparametrized beta, Rayleigh, shifted Gompertz, Skellam, slash, triangular, truncated binomial, truncated normal, truncated Poisson, Tukey lambda, Wald, zero-inflated binomial, zero-inflated negative binomial, zero-inflated Poisson.

Maintained by Tymoteusz Wolodzko. Last updated 25 days ago.

c-plus-plus c-plus-plus-11 distribution multivariate-distributions probability random-generation rcpp statistics cpp

53 stars 11.60 score 1.5k scripts 107 dependents

tyee001

VGAM:Vector Generalized Linear and Additive Models

An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes.

Maintained by Thomas Yee. Last updated 2 months ago.

fortran

10 stars 10.67 score 3.6k scripts 169 dependents

lbelzile

mev:Modelling of Extreme Values

Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.

Maintained by Leo Belzile. Last updated 5 months ago.

extreme-value-statistics likelihood-functions max-stable simulation threshold-selection openblas cpp openmp

14 stars 8.21 score 94 scripts 4 dependents

cran

evd:Functions for Extreme Value Distributions

Extends simulation, distribution, quantile and density functions to univariate and multivariate parametric extreme value distributions, and provides fitting functions which calculate maximum likelihood estimates for univariate and bivariate maxima models, and for univariate and bivariate threshold models.

Maintained by Alec Stephenson. Last updated 6 months ago.

2 stars 7.58 score 84 dependents

paulnorthrop

revdbayes:Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis

Provides functions for the Bayesian analysis of extreme value models. The 'rust' package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution. The functionality of 'revdbayes' is similar to the 'evdbayes' package <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain Monte Carlo ('MCMC') methods for posterior simulation. In addition, there are functions for making inferences about the extremal index, using the models for threshold inter-exceedance times of Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3>. Also provided are d,p,q,r functions for the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') distributions that deal appropriately with cases where the shape parameter is very close to zero.

Maintained by Paul J. Northrop. Last updated 7 months ago.

analysis bayesian extreme extreme-value-statistics extremes generalized-pareto-distribution gev inference nhpp point-process posterior predictive rcpp value openblas cpp

4 stars 7.41 score 58 scripts 4 dependents

r-forge

fExtremes:Rmetrics - Modelling Extreme Events in Finance

Provides functions for analysing and modelling extreme events in financial time Series. The topics include: (i) data pre-processing, (ii) explorative data analysis, (iii) peak over threshold modelling, (iv) block maxima modelling, (v) estimation of VaR and CVaR, and (vi) the computation of the extreme index.

Maintained by Paul J. Northrop. Last updated 10 days ago.

1 stars 7.29 score 118 scripts 4 dependents

tychelab

CoSMoS:Complete Stochastic Modelling Solution

Makes univariate, multivariate, or random fields simulations precise and simple. Just select the desired time series or random fields’ properties and it will do the rest. CoSMoS is based on the framework described in Papalexiou (2018, <doi:10.1016/j.advwatres.2018.02.013>), extended for random fields in Papalexiou and Serinaldi (2020, <doi:10.1029/2019WR026331>), and further advanced in Papalexiou et al. (2021, <doi:10.1029/2020WR029466>) to allow fine-scale space-time simulation of storms (or even cyclone-mimicking fields).

Maintained by Kevin Shook. Last updated 4 years ago.

11 stars 7.10 score 77 scripts

harrysouthworth

texmex:Statistical Modelling of Extreme Values

Statistical extreme value modelling of threshold excesses, maxima and multivariate extremes. Univariate models for threshold excesses and maxima are the Generalised Pareto, and Generalised Extreme Value model respectively. These models may be fitted by using maximum (optionally penalised-)likelihood, or Bayesian estimation, and both classes of models may be fitted with covariates in any/all model parameters. Model diagnostics support the fitting process. Graphical output for visualising fitted models and return level estimates is provided. For serially dependent sequences, the intervals declustering algorithm of Ferro and Segers (2003) <doi:10.1111/1467-9868.00401> is provided, with diagnostic support to aid selection of threshold and declustering horizon. Multivariate modelling is performed via the conditional approach of Heffernan and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>, with graphical tools for threshold selection and to diagnose estimation convergence.

Maintained by Harry Southworth. Last updated 1 years ago.

cpp

7 stars 6.44 score 66 scripts 1 dependents

bpfaff

evir:Extreme Values in R

Functions for extreme value theory, which may be divided into the following groups; exploratory data analysis, block maxima, peaks over thresholds (univariate and bivariate), point processes, gev/gpd distributions.

Maintained by Bernhard Pfaff. Last updated 9 years ago.

2 stars 5.89 score 211 scripts 6 dependents

r-forge

SpatialExtremes:Modelling Spatial Extremes

Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.

Maintained by Mathieu Ribatet. Last updated 11 months ago.

openblas

5.36 score 189 scripts 2 dependents

jlilienthal

TLMoments:Calculate TL-Moments and Convert Them to Distribution Parameters

Calculates empirical TL-moments (trimmed L-moments) of arbitrary order and trimming, and converts them to distribution parameters.

Maintained by Jona Lilienthal. Last updated 3 years ago.

cpp

4.79 score 29 scripts 22 dependents

yrobink

ROOPSD:R Object Oriented Programming for Statistical Distribution

Statistical distribution in OOP (Object Oriented Programming) way. This package proposes a R6 class interface to classic statistical distribution, and new distributions can be easily added with the class AbstractDist. A useful point is the generic fit() method for each class, which uses a maximum likelihood estimation to find the parameters of a dataset, see, e.g. Hastie, T. and al (2009) <isbn:978-0-387-84857-0>. Furthermore, the rv_histogram class gives a non-parametric fit, with the same accessors that for the classic distribution. Finally, three random generators useful to build synthetic data are given: a multivariate normal generator, an orthogonal matrix generator, and a symmetric positive definite matrix generator, see Mezzadri, F. (2007) <arXiv:math-ph/0609050>.

Maintained by Yoann Robin. Last updated 2 years ago.

1 stars 4.49 score 5 scripts 12 dependents

tpetzoldt

FAdist:Distributions that are Sometimes Used in Hydrology

Probability distributions that are sometimes useful in hydrology.

Maintained by Thomas Petzoldt. Last updated 3 years ago.

4 stars 4.49 score 51 scripts 1 dependents

alexcannon

GEVcdn:GEV Conditional Density Estimation Network

Implements a flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology following Cannon (2010) <doi:10.1002/hyp.7506>.

Maintained by Alex J. Cannon. Last updated 5 years ago.

1 stars 1.00 score 5 scripts