Bayesian Non- and Semi-parametric Model Fitting
Markov chain Monte Carlo algorithms for non- and semi-parametric models

Bayesian Non- and Semi-Parametric Model Fitting
Markov chain Monte Carlo algorithms for non- and semi-parametric models.

Bayesian Non- And Semi-Parametric Model Fitting

coda, ggplot2, plot3D, threejs, gridExtra

Markov chain Monte Carlo algorithms for non- and semi-parametric models: 1. Dirichlet process mixtures & 2. spike-slab for variable selection in mean/variance regression models.

coda, ggplot2, plot3D, threejs, gridExtra, cubature, Formula,
plyr

coda, ggplot2, plot3D, threejs, gridExtra, cubature, Formula,
plyr,mgcv

Markov chain Monte Carlo algorithms for non- and semi-parametric models: 1. Dirichlet process mixtures & 2. spike-slab priors for variable selection mean/variance regression models, for univariate and multivariate response models.
coda, ggplot2, plot3D, threejs, gridExtra, cubature, Formula,
plyr, mgcv, corrplot

MCMC algorithms & processing functions for non- and semi-parametric models: 1. Dirichlet process mixtures & 2. spike-slab for multivariate (and univariate) response analysis, with nonparametric models for the means, the variances and the correlation matrix.

MCMC algorithms & processing functions for non- and semi-parametric models: 1. Dirichlet process mixtures & 2. spike-slab for multivariate (and univariate) regression, with nonparametric models for the means, the variances and the correlation matrix.

MCMC algorithms & processing functions for: 1. multivariate (and univariate) regression, with nonparametric models for the means, the variances and the correlation matrix, with variable selection, and 2. Dirichlet process mixtures.
coda, ggplot2, plot3D, threejs, gridExtra, cubature, Formula,
plyr, mgcv, corrplot, label.switching

MCMC algorithms & processing functions for: 1. single response multiple regression, see Papageorgiou, G. (2018) <doi: 10.32614/RJ-2018-069>, 2. multivariate response multiple regression, with nonparametric models for the means, the variances and the correlation matrix, with variable selection, see Papageorgiou, G. and Marshall, B. C. (2020) <doi: 10.1080/10618600.2020.1739534>, 3. joint mean-covariance models for multivariate responses, see Papageorgiou, G. (2020), and 4.Dirichlet process mixtures, see Papageorgiou, G. (2019) <doi: 10.1111/anzs.12273>.