Code and data Mark F.J. Steel
Please note that all code is provided "as is". I am afraid neither myself nor my coauthors can provide any support. You are welcome to download and use the code (and data) for your own research, provided you acknowledge this and cite our work accordingly.
- Fortran code and data for paper: On the estimation of demand systems through consumption efficiency
- Fortran code for paper: Bayesian analysis of long memory and persistence using ARFIMA models
- Fortran code and data for paper: Bayesian modelling of catch in a Northwest Atlantic Fishery
- Fortran code and data for paper: Benchmark priors for Bayesian model averaging
- Fortran code and data for paper: Model uncertainty in cross-country growth regressions (alternative link to final version)
- Matlab code and C++ (Unix) code for paper: Semiparametric Bayesian Inference for Stochastic Frontier Models
- Matlab code (with readme file) for paper: Model comparison of coordinate-free multivariate skewed distributions with an application to stochastic frontiers
- Fortran code and data (with "readme" file) for paper: Non-Gaussian Bayesian Geostatistical Modelling
- WinBUGS code and data (with "readme" file) for paper: Bayesian Stochastic Frontier Analysis Using WinBUGS
- Fortran code and data and supplementary material for paper: Jointness in Bayesian variable selection with applications to growth regression
- Matlab code and data for the paper: Non-Gaussian Dynamic Bayesian Modelling for Panel Data
- Matlab code: MCMC sampler for non-Gaussian cluster model, data sets and code for the bridge sampler for paper: Model-based clustering of non-Gaussian panel data based on skew-t distributions
- Code and data sets with readme file for paper: On the effect of prior assumptions in Bayesian Model Averaging with applications to growth regression
- Code (in Ox) and data with readme file for paper: A General Class of Nonseparable Space-time Covariance Models
- Code (in Matlab) and data with readme file for paper: Transdimensional sampling algorithms for Bayesian variable selection in classification problems with many more variables than observations
- Code (in Matlab) and data with readme file for paper: Cross-validation prior choice in Bayesian probit regression with many covariates
- Mathematica notebooks for moments and crossmoments with readme file for paper: Modelling overdispersion with the Normalized Tempered Stable Distribution
- R Code and data for paper: Inference in Two-Piece Location-Scale Models with Jeffreys Priors
- R code for the case of independent X and Y using two-piece normal and skew-normal marginals for paper: Bayesian inference for P(X<Y) using asymmetric dependent distributions, CRiSM Working Paper 11-31
- R code with readme file for DTP distributions as in: Bayesian modelling of skewness and kurtosis with two-piece scale and shape transformations, CRiSM Working Paper 13-10
- Matlab code with readme file and data for paper: Adaptive MC^3 and Gibbs Algorithms for Bayesian Model Averaging in Linear Regression Models, CRiSM Working Paper 13-11
- R code (zipped) with data and description file for paper: Objective Bayesian survival analysis using scale mixtures of log-normal distributions, CRiSM Working Paper 13-01
- R code and supplementary material for paper: Bayesian survival modelling of university outcomes, WRAP link
- R code (zipped) and supplementary material for paper: Incorporating unobserved heterogeneity in Weibull survival models: A Bayesian approach, CRiSM Working Paper 14-20
- Matlab code for paper: Individual adaptation: an adaptive MCMC scheme for variable selection problems, CRiSM Working Paper 15-01
Software provided by others:
- Skew-t package in R by Robert King for skew-t distribution as in the paper: On Bayesian modelling of fat tails and skewness (JSTOR link), See also the R code by Venturini and Myers.
- R code by Stefan Zeugner and Martin Feldkircher for Bayesian Model Averaging as in the paper: On the effect of prior assumptions in Bayesian Model Averaging with applications to growth regression. Zeugner and Feldkircher also have a more general resource page for BMA.
- R package with manual by F. J. Rubio and A. M. Lopez for two-piece distributions as in the paper: Inference in Two-Piece Location-Scale Models with Jeffreys Priors .