BayES is still at an early development stage. The models that are supported are:

• linear models for cross-sectional and panel data:
  • simple linear model
  • random-effects model
  • random-coefficients models
  • latent-class models
• stochastic frontier models (production and cost specifications):
  • simple stochastic frontiers
  • inefficiency-effects models
  • random-effects stochastic frontiers
  • random-coefficients stochastic frontiers
  • latent-class models
  • dynamic stochastic frontiers, with and without random effects
• discrete-choice models:
  • simple Probit and Logit models
  • random-effects Probit and Logit models
  • multivariate Probit model
  • multinomial and conditional Probit and Logit models
• models where the dependent variable has an ordinal interpretation:
  • simple ordered Probit and Logit models
• models for count data:
  • simple Poisson and negative-Binomial models
• models for censored and truncated variables:
  • simple type I Tobit models
  • simple type II Tobit models
• models for systems of equations:
  • simple Seemingly Unrelated Regressions (SUR) models
• time-series models:
  • Vector Autoregressive (VAR) models

See the documentation section of this website for more details.

The following models are planned for future inclusion in BayES:

• linear models:
  • Markov-switching models for time-series and panel data
  • State-Space models for time-series and panel data
• stochastic frontier models:
  • Markov-switching models for panel data
• discrete-choice models:
  • random-coefficients Probit and Logit models
  • random-effects multinomial and conditional Probit and Logit models
  • nested Logit models
• models for censored and truncated variables:
  • hurdle models
  • models for truncated regression
• models for count data
  • zero-inflated Poisson models
  • Poisson and negative-Binomial models with random effects
• time-series models:
  • ARMA and ARIMA models
  • univariate and multivariate ARCH and GARCH models
  • Vector Error Correction (VEC) models
  • univariate and multivariate stochastic-volatility models

Development is currently focused on the implementation of latent-class models. If you would like to see particular models or features in BayES or if you have a preference on which of the models presented above should be implemented first, consider expressing your views by taking a short survey.

BayES is written entirely in c++. The graphical user interface is built using wxWidgets. BayES proudly uses Eigen for its matrix calculations and parts of the Boost c++ libraries. ANTLR is used to parse BayES' language and PLplot is used for plotting.

Estimation is performed using Gibbs samplers, whenever possible. If the full conditional for a parameter does not belong to any known family, then BayES uses case-specific Metropolis-Hastings updates.

BayES runs on 32-bit and 64-bit Microsoft^® Windows^® systems (Windows 7, 8, and 10), on 32-bit and 64-bit Linux systems and on 64-bit macOS systems

The following table contains links to the latest-release installers. Previous releases can be found in the downloads section of the website. Step-by-step instructions for installing BayES on Microsoft^® Windows^® or Linux systems are provided in Appendix A of the documentation.

If you download and use BayES, please consider providing feedback. Positive feedback is always welcome and constructive criticism much appreciated. You can provide feedback by taking a short survey, by email or by commenting on the project's page on ResearchGate.