Factor Copula Models
BB1 copula parameter (theta,delta) to tail dependence parameters
BB1, given 0<tau<1, find theta and delta with lower tail dependence eq...
BB1 tail dependence parameters to copula parameter (theta,delta)
Gaussian bi-factor structure correlation matrix
log-likelihood Gaussian bi-factor structure correlation matrix
Bi-factor partial correlations to correlation matrix version 2, using ...
Bi-factor partial correlations to correlation matrix
negative log-likelihood of bi-factor structured factor copula and deri...
Sequential parameter estimation for bi-factor copula with estimated la...
Proxies for bi-factor copula model based on Gaussian bi-factor score
Kendall's tau for bivariate normal
Semi-correlation for bivariate normal/Gaussian distribution
Discrepancy of model-based and observed correlation matrices based on ...
Convert from correlations in vector form to a correlation matrix
lower and upper bounds for copula parameters (1-parameter, 2-parameter...
Integrand for 1-factor copula with 1-parameter bivariate linking copul...
log returns and GARCH-filtered log returns for some Euro markets 2007
negative log-likelihood with gradient and Hessian computed in f90 for ...
Frank: Blomqvist's beta to copula parameter
Frank: Spearman rho to copula parameter
Compute correlation matrix according to 1-factor + 1-truncated vine (r...
R interface for Gauss-Legendre quadrature
Gumbel: Blomqvist's beta to copula parameter
Gumbel: Spearman rho to copula parameter
Check if a square symmetric matrix is positive definite
Compute new proxies for 1-factor copula based on the mean of observati...
Compute new proxies for 1-factor copula based on the mean of observati...
Conditional expectation proxies for bi-factor copula models with linki...
min negative log-likelihood for 1-factor copula with nlm()
min negative log-likelihood for 1-factor copula model (some parameters...
max likelihood (min negative log-likelihood) for 1-factor copula model
negative log-likelihood for the bi-factor Gaussian/t model
MLE for multivariate normal/t with a bi-factor or nested factor correl...
negative log-likelihood for the p-factor Gaussian/t model
MLE in a MVt model with a p-factor correlation structure
negative log-likelihoods of nested factor structured factor copula and...
Rank-based normal scores transform
Gaussian oblique factor structure correlation matrix
Gaussian oblique factor structure correlation matrix
log-likelihood Gaussian oblique factor structure correlation matrix
log-likelihood Gaussian oblique factor structure correlation matrix
oblique factor correlation structure for d variables and m groups
oblique factor correlation structure for d variables and m groups incl...
negative log-likelihood of 1-factor copula for input to posDefHessMin ...
Parameter estimation for 1-factor copula with estimated latent variabl...
Partial correlation representation to loadings for p-factor
Gaussian p-factor structure correlation matrix
log-likelihood Gaussian p-factor structure correlation matrix
Minimization with modified Newton-Raphson iterations, Hessian is modif...
Version with ifixed as argument
C_[2|1]^-1 for bivariate Student t copula
C_[2|1]^-1 for bivariate Frank copula
simulate from 1-factor copula model with different linking copula fami...
simulate from bi-factor copula model
correlation matrix for 1-factor plus 1-truncated vine (for residual de...
Spearman's rho for bivariate copula with parameter cpar
Random multivariate normal (standard N(0,1) margins)
Random multivariate t (standard t(nu) margins)
Simulate data from nested copula or Gaussian model
compute correlation matrix from 2-truncated R-vine
Semi-correlations for two variables
Semi-correlation table for a multivariate data set
Tail dependence parameter estimation
Rank-based uniform scores transform
Empirical version of zeta(alpha) tail-weighted dependence measure
Upper Tail-weighted dependence measure zeta(C,alpha)
Plot zeta(alpha) against alpha
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.