The bayesmh command has a number of enhancements:

The new prior mvnscaled() provides a multivariate normal prior with a scaled covariance matrix. The new distribution can be used to specify a conjugate prior for the regression coefficients of a linear regression model.

Gibbs sampling is now available for the combination of a probit likelihood and a multivariate normal prior for regression coefficients.

Time-series operators are now allowed with independent variables in linear, nonlinear, and multiequation models.