Package: FactorCopulaModel 0.1.1

Pavel Krupskii

FactorCopulaModel: Factor Copula Models

Inference methods for factor copula models for continuous data in Krupskii and Joe (2013) <doi:10.1016/j.jmva.2013.05.001>, Krupskii and Joe (2015) <doi:10.1016/j.jmva.2014.11.002>, Fan and Joe (2024) <doi:10.1016/j.jmva.2023.105263>, one factor truncated vine models in Joe (2018) <doi:10.1002/cjs.11481>, and Gaussian oblique factor models. Functions for computing tail-weighted dependence measures in Lee, Joe and Krupskii (2018) <doi:10.1080/10485252.2017.1407414> and estimating tail dependence parameter.

Authors:Harry Joe [aut], Pavel Krupskii [aut, cre], Xinyao Fan [aut], Allan Macleod [cph], Robert Gentleman [cph], Ross Ihaka [cph]

FactorCopulaModel_0.1.1.tar.gz
FactorCopulaModel_0.1.1.tar.gz(r-4.7-arm64)FactorCopulaModel_0.1.1.tar.gz(r-4.7-x86_64)FactorCopulaModel_0.1.1.tar.gz(r-4.6-arm64)FactorCopulaModel_0.1.1.tar.gz(r-4.6-x86_64)
FactorCopulaModel_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
FactorCopulaModel/json (API)

# Install 'FactorCopulaModel' in R:
install.packages('FactorCopulaModel', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • fortran– Runtime library for GNU Fortran applications
Datasets:
  • dateindex - GARCH-filtered log returns for Dow Jones stocks 2014-2016
  • dj1416gf - GARCH-filtered log returns for Dow Jones stocks 2014-2016
  • euro07gf - Log returns and GARCH-filtered log returns for some Euro markets 2007
  • euro07names - Log returns and GARCH-filtered log returns for some Euro markets 2007
  • lab - GARCH-filtered log returns for Dow Jones stocks 2014-2016
  • rainstorm - Precipitation by rainstorm at 28 stations

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

fortranglibc

1.30 score 295 downloads 64 exports 17 dependencies

Last updated from:f2daf20e8d. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK138
linux-devel-x86_64OK113
source / vignettesOK153
linux-release-arm64OK146
linux-release-x86_64OK132
wasm-releaseOK148

Exports:bb1_cpar2tdbb1_tau2eqtdbb1_td2cparbifactor_fabifactor_nllkbifactor2corbifactor2cor_v2bifactorcop_nllkbifactorEstWithProxybifactorScorebvn_cpar2taubvnSemiCorcorDiscorvec2matcparBoundsd1factcopfactor1trvine_nllkfrank_beta2cparfrank_rhoS2cpargauss1f1tgaussLegendregumbel_beta2cpargumbel_rhoS2cparisPosDeflatentUpdate1factorlatentUpdate1factor1latentUpdateBifactorml1factorml1factor_f90ml1factor_v2mvtBifactmvtBifact_nllkmvtPfactmvtPfact_nllknestfactorcop_nllknscoreoblique_faoblique_grad_faoblique_grad_nllkoblique_nllkoblique_par2loadoblique_pp_par2loadonefactorcop_nllkonefactorEstWithProxypcor2loadpfactor_fapfactor_nllkposDefHessMinposDefHessMinbqcondbvtcopqcondFrankr1factorrbifactorrhoSrmvnrmvtrnestfactorsemiCorsemiCorTabletailDepuscorezetaDepzetaDepCzetaPlot

Dependencies:ADGofTestclicpp11cubatureglueigraphlatticelifecyclemagrittrMASSMatrixmvtnormpkgconfigRcpprlangvctrsVineCopula

Readme and manuals

Help Manual

Help pageTopics
BB1 copula parameter (theta,delta) to tail dependence parametersbb1_cpar2td
BB1, given 0<tau<1, find theta and delta with lower tail dependence equal upper tail dependencebb1_tau2eqtd
BB1 tail dependence parameters to copula parameter (theta,delta)bb1_td2cpar
Gaussian bi-factor structure correlation matrixbifactor_fa
log-likelihood Gaussian bi-factor structure correlation matrixbifactor_nllk
Bi-factor partial correlations to correlation matrixbifactor2cor
Bi-factor partial correlations to correlation matrix version 2, using the inverse and determinant of a smaller matrixbifactor2cor_v2
negative log-likelihood of bi-factor structured factor copula and derivatives computed in f90 for input to posDefHessMinbbifactorcop_nllk
Sequential parameter estimation for bi-factor copula with estimated latent variables using VineCopula::BiCopSelectbifactorEstWithProxy
Proxies for bi-factor copula model based on Gaussian bi-factor scorebifactorScore
Kendall's tau for bivariate normalbvn_cpar2tau
Semi-correlation for bivariate normal/Gaussian distributionbvnSemiCor
Discrepancy of model-based and observed correlation matrices based on Gaussian log-likelihoodcorDis
Convert from correlations in vector form to a correlation matrixcorvec2mat
lower and upper bounds for copula parameters (1-parameter, 2-parameter families)cparBounds
Integrand for 1-factor copula with 1-parameter bivariate linking copula families; or for m-parameter bivariate linking copulasd1factcop
GARCH-filtered log returns for Dow Jones stocks 2014-2016dateindex dj1416gf DJ20142016gf lab
log returns and GARCH-filtered log returns for some Euro markets 2007euro07 euro07gf euro07names
negative log-likelihood with gradient and Hessian computed in f90 for copula from 1-factor/1-truncated vine (tree for residual dependence conditional on a latent variable); models included are BB1 for latent with Frank or Gaussian(bvncop) for truncated vine residual dependencefactor1trvine_nllk
Frank: Blomqvist's beta to copula parameterfrank_beta2cpar
Frank: Spearman rho to copula parameterfrank_rhoS2cpar
Compute correlation matrix according to 1-factor + 1-truncated vine (residual dependence) modelgauss1f1t
R interface for Gauss-Legendre quadraturegaussLegendre
Gumbel: Blomqvist's beta to copula parametergumbel_beta2cpar
Gumbel: Spearman rho to copula parametergumbel_rhoS2cpar
Check if a square symmetric matrix is positive definiteisPosDef
Compute new proxies for 1-factor copula based on the mean of observationslatentUpdate1factor
Compute new proxies for 1-factor copula based on the mean of observationslatentUpdate1factor1
Conditional expectation proxies for bi-factor copula models with linking copulas in different copula familieslatentUpdateBifactor
max likelihood (min negative log-likelihood) for 1-factor copula modelml1factor
min negative log-likelihood for 1-factor copula with nlm()ml1factor_f90
min negative log-likelihood for 1-factor copula model (some parameters can be fixed)ml1factor_v2
MLE for multivariate normal/t with a bi-factor or nested factor correlation structuremvtBifact
negative log-likelihood for the bi-factor Gaussian/t modelmvtBifact_nllk
MLE in a MVt model with a p-factor correlation structuremvtPfact
negative log-likelihood for the p-factor Gaussian/t modelmvtPfact_nllk
negative log-likelihoods of nested factor structured factor copula and derivatives computed in f90 for input to posDefHessMinbnestfactorcop_nllk
Rank-based normal scores transformnscore
Gaussian oblique factor structure correlation matrixoblique_fa
Gaussian oblique factor structure correlation matrixoblique_grad_fa
log-likelihood Gaussian oblique factor structure correlation matrixoblique_grad_nllk
log-likelihood Gaussian oblique factor structure correlation matrixoblique_nllk
oblique factor correlation structure for d variables and m groupsoblique_par2load
oblique factor correlation structure for d variables and m groups include determinant and inverseoblique_pp_par2load
negative log-likelihood of 1-factor copula for input to posDefHessMin and posDefHessMinbonefactorcop_nllk
Parameter estimation for 1-factor copula with estimated latent variables using VineCopula::BiCopSeelctonefactorEstWithProxy
Partial correlation representation to loadings for p-factorpcor2load
Gaussian p-factor structure correlation matrixpfactor_fa
log-likelihood Gaussian p-factor structure correlation matrixpfactor_nllk
Minimization with modified Newton-Raphson iterations, Hessian is modified to be positive definite at each step. Algorithm and code produced by Pavel Krupskii (2013) see PhD thesis Krupskii (2014), UBC and Section 6.2 of # Joe (2014) Dependence Models with Copulas. Chapman&Hall/CRCposDefHessMin
Version with ifixed as argumentposDefHessMinb
C_[2|1]^[-1](p|u) for bivariate Student t copulaqcondbvtcop
C_[2|1]^[-1](p|u) for bivariate Frank copulaqcondFrank
simulate from 1-factor copula model with different linking copula familiesr1factor
Precipitation by rainstorm at 28 stationsrainstorm
simulate from bi-factor copula modelrbifactor
correlation matrix for 1-factor plus 1-truncated vine (for residual dependence)residDep
Spearman's rho for bivariate copula with parameter cparrhoS
Random multivariate normal (standard N(0,1) margins)rmvn
Random multivariate t (standard t(nu) margins)rmvt
Simulate data from nested copula or Gaussian modelrnestfactor
compute correlation matrix from 2-truncated R-vineRVtrunc2cor
Semi-correlations for two variablessemiCor
Semi-correlation table for a multivariate data setsemiCorTable
Tail dependence parameter estimationtailDep
Rank-based uniform scores transformuscore
Empirical version of zeta(alpha) tail-weighted dependence measurezetaDep
Upper Tail-weighted dependence measure zeta(C,alpha)zetaDepC
Plot zeta(alpha) against alphazetaPlot