Struct MultiFactorGaussianCopula 

pub struct MultiFactorGaussianCopula { /* private fields */ }

Multi-factor Gaussian copula.

Generalizes the one-factor model to K common factors: X_i = Σ_k β_{ik} * Z_k + sqrt(1 - Σ_k β_{ik}^2) * ε_i

Implementations

impl MultiFactorGaussianCopula

pub fn new(loadings: DMatrix<f64>) -> Result<Self>

Create a new multi-factor Gaussian copula.

Arguments

• loadings - Loading matrix (dimension × num_factors)

Returns

A new multi-factor Gaussian copula

pub fn correlation_matrix(&self) -> DMatrix<f64>

Get the correlation matrix implied by the factor loadings.

Trait Implementations

impl Clone for MultiFactorGaussianCopula

fn clone(&self) -> MultiFactorGaussianCopula

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Copula for MultiFactorGaussianCopula

fn cdf(&self, u: &[f64]) -> Result<f64>

Evaluate the copula cumulative distribution function (CDF) at point u.

fn pdf(&self, u: &[f64]) -> Result<f64>

Evaluate the copula probability density function (PDF) at point u.

fn sample<R: Rng + ?Sized>(&self, n: usize, rng: &mut R) -> Result<DMatrix<f64>>

Generate random samples from the copula.

fn dimension(&self) -> usize

Get the dimension of the copula.

fn conditional_cdf(&self, u: &[f64], given: &[usize]) -> Result<f64>

Compute the conditional copula CDF given some variables.

fn tail_dependence(&self) -> Result<(f64, f64)>

Compute the tail dependence coefficients.

fn kendall_tau(&self) -> Result<f64>

Compute Kendall’s tau for this copula.

fn spearman_rho(&self) -> Result<f64>

Compute Spearman’s rho for this copula.

fn has_closed_form(&self) -> (bool, bool)

Check if the copula has analytical forms for CDF and PDF.

fn family_name(&self) -> &'static str

Get a string identifier for the copula family.

impl Debug for MultiFactorGaussianCopula

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.