To see *MacMCMC* in action, consider this famous example from the literature (Arnold and Libby, 1949):

## InputData |
## OutputReport |

Given the *MacMCMC* report, any graphing software may be used to prepare a plot showing model versus data.

Note: The blue line in this plot uses mean estimates; the red line shows the prior uncertainty for parameter A.

- Complete, standalone Mac application
- 100% Bayesian inference
- 100% ensemble MCMC
- Access to low-level options
- Parallelized for maximum speed

- Input data and model with ASCII plaintext (UTF-8)
- Live editing of model
- 27 built-in distributions
- 16 continous (incl. BivariateNormal)
- 8 discrete
- 3 (homogeneous) mixtures
- Normal and BivariateNormal
- Poisson
- Generic (user-defined) distribution
- 15 built-in functions
- [Optional] "Extras" category for ancillary quantities
- Output files for monitored variables
- Report
- MAP, Mean, Median, Mode and Gelman-Rubin statistic
- 90, 95 and 99-percent credible (HPD) intervals
- [Optional] Computation of log(marginal likelihood)
- Trace (thinned, tabular posterior)
- Plots of marginals
- Output as pdf and/or png
- [Optional] Editing of axis labels
- [Optional] Fourier smoothing of plot
- Trace comparison for selected chains
- [Optional] Special handling for built-in mixtures
- Automatic relabeling
- Output of class probabilities for datapoints
- [Optional] Goodness-of-fit plots (in most cases)
- Equations: X-Y plot
- Continuous distributions: quantile-quantile (q-q) plot
- Discrete distributions: PDF histogram
- [Optional] 95-percent posterior-predictive credible interval band
- Examples:

For those who prefer a simpler, frequentist application, Regress+ 2.8 is still available here.