Reference

Package Information
CMA	CMA: Copula-Marginal Algorithm
CMA Algorithm A two-step recipe to manipulate multivariate distributions
cma_separation()	CMA Separation Step
cma_combination()	CMA combination Step
Marginal Distributions Tools to manipulate the unique characteristics of a distribution
fit_ghd()	Estimation of the Generalized Hyperbolic Distribution
fit_hyp()	Estimation of the Hyperbolic Distribution
fit_nig()	Estimation of the Normal-Inverse Gaussian Distribution
fit_normal()	Estimation of the Normal Distribution
fit_t()	Estimation of the Student-t Distribution
fit_vg()	Estimation of the Variance-Gamma Distribution
Copulas Tools to manipulate the joint characteristics of a distribution
copula_transformation()	Copula Normalization
fit_copula_clayton() fit_copula_gumbel() fit_copula_frank() fit_copula_t() fit_copula_normal() fit_copula_joe()	Archimedean Copulas for Scenario Analysis
Generate Build scenarios from fitted objects
generate_margins()	Generate Marginal Distributions
generate_copulas()	Generate Copulas
Moment-Matching An algorithm to match exactly the location and dispersion parameters of a distribution
match_normal()	Moment Matching for the Normal Distribution
match_t()	Moment Matching for the t Distribution
Panic Generate ex-ante worst case scenarios via entropy-pooling
panic_copula()	Panic-Copula for Stress-Testing
plot_panic_distribution()	Visualize a Panic Distribution
empirical_stats()	Summary Statistics for Empirical Distributions
Utilities Additional functions
entropy_pooling()	Numerical Entropy Minimization
fp_moments()	Moments under Flexible Probabilities