Imports: rstan (>= 2.21.2), rstantools (>= 2.1.1), ggplot2, MASS,

Description: Fit Bayesian multivariate GARCH models using 'Stan' for full Bayesian inference. Generate (weighted) forecasts for means, variances (volatility) and correlations. Currently DCC(P,Q), CCC(P,Q), pdBEKK(P,Q), and BEKK(P,Q) parameterizations are implemented, based either on a multivariate gaussian normal or student-t distribution. DCC and CCC models are based on Engle (2002) <doi:10.1198/073500102288618487> and Bollerslev (1990). The BEKK parameterization follows Engle and Kroner (1995) <doi:10.1017/S0266466600009063> while the pdBEKK as well as the estimation approach for this package is described in Rast et al. (2020) <doi:10.31234/osf.io/j57pk>. The fitted models contain 'rstan' objects and can be examined with 'rstan' functions.
Imports: forecast, ggplot2, loo, MASS, Rdpack, rstan (>= 2.19.2),

Title: Bayesian Multidimensional Generalized Graded Unfolding Model
Description: Full Bayesian estimation of Multidimensional Generalized Graded Unfolding Model (MGGUM) using 'rstan' (See Stan Development Team (2020) <https://mc-stan.org/>).
Functions are provided for estimation, result extraction, model fit statistics, and plottings.
2.18.1), rstantools (>= 2.1.1), edstan, ggplot2, GGUM, loo,

Title: Bayesian Estimation for Finite Mixture of Distributions
Description: Provides statistical tools for Bayesian estimation for finite mixture of distributions, mainly mixture of Gamma, Normal and t-distributions.

Description: Provides statistical tools for Bayesian estimation for finite mixture of distributions, mainly mixture of Gamma, Normal and t-distributions. The package is implemented the recent improvements in Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) <doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012) <doi:10.1080/03610918.2011.588358>.

Description: Provides statistical tools for Bayesian estimation for finite mixture of distributions, mainly mixture of Gamma, Normal and t-distributions. The package is implemented the recent improvements in Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) <https://doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012) <https://doi:10.1080/03610918.2011.588358>.