MixedIndTests - Tests of Randomness and Tests of Independence

Functions for testing randomness for a univariate time series with arbitrary distribution (discrete, continuous, mixture of both types) and for testing independence between random variables with arbitrary distributions. The test statistics are based on the multilinear empirical copula and multipliers are used to compute P-values. The test of independence between random variables appeared in Genest, Nešlehová, Rémillard & Murphy (2019) and the test of randomness appeared in Nasri (2022).

Last updated 1 years ago

1.48 score 1 dependents 6 scripts 866 downloads

GaussianHMM1d - Inference, Goodness-of-Fit and Forecast for Univariate Gaussian Hidden Markov Models

Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.

Last updated 1 months ago

1.08 score 12 scripts 400 downloads

CopulaInference - Estimation and Goodness-of-Fit of Copula-Based Models with Arbitrary Distributions

Estimation and goodness-of-fit functions for copula-based models of bivariate data with arbitrary distributions (discrete, continuous, mixture of both types). The copula families considered here are the Gaussian, Student, Clayton, Frank, Gumbel, Joe, Plackett, BB1, BB6, BB7,BB8, together with the following non-central squared copula families in Nasri (2020) <doi:10.1016/j.spl.2020.108704>: ncs-gaussian, ncs-clayton, ncs-gumbel, ncs-frank, ncs-joe, and ncs-plackett. For theoretical details, see, e.g., Nasri and Remillard (2023) <arXiv:2301.13408>.

Last updated 2 years ago

1.00 score 719 downloads