TrackStar is a highly optimized, user-friendly library designed to compute Bayesian likelihood estimates for arbitrary lines and curves when the data have errors in every direction. When combined with your favorite optimization routine (e.g. Markov chain Monte Carlo, maximum a posteriori esimation), TrackStar provides blazing fast best-fit parameters!
TrackStar users enjoy the following features:
- Support for samples in which some quantities are not available for every data vector. For example, if θ1 and θ2 are available for the whole sample, but only half of the data have measurements of θ3, TrackStar will automatically use θ3 where it is available.
- Multi-threading with the OpenMP library. If these features are enabled when TrackStar is installed, simply tell it how many threads you'd like it to use, and hit run!
- Full control over the expected N-dimensional distribution of the data, both due to selection effects and arising from model predictions.
- Finish implementing unit tests
- Ensure the likelihood estimates is properly normalized when the data vectors do not have the same dimensionality (i.e. when not all quantities are measured for each datum).
- Finish writing documentation: API reference, science documentation, installation instructions, developers documentation, Changelog
- Write example codes