Introduction
bayes_traj is a software package written in Python that provides routines for performing Bayesian trajectory modeling of longitudinal data. Multiple, longitudinally observed target variables -- continuous, binary, or a combination -- can be modeled simultaneously. Per-trajectory random effects can also be modeled for continuous target variables. This package also provides command-line tools that facilitate spefication of Bayesian priors, enable visualization of trajectory modeling results, and compute summary and model fit statistics.
Installation
In order to install the package, type the folowing in the terminal:
$ pip install bayes_traj
Overview
bayes_traj provides several command-line tools:
generate_prior-- used to speficy Bayesian priors for use the trajectory modelingviz_data_prior_draws-- provides visualization of random draws from the priorbayes_traj_main-- performs Bayesian trajectory modeling using a prior fileviz_model_trajs-- provides visualization of trajectories fit usingbayes_traj_mainsumarize_traj_model-- prints model summary and fit statistics given a model file produce bybayes_traj_mainassign_trajectory-- writes a data file with appended trajectory assignment information given an input data file and a model file generated by thebayes_traj_maintool
Each of these tools can be run with the -h flag for additional usage information.
For additional documentation, see https://acil-bwh.github.io/bayes_traj/index.html
Tests
To run all unit tests, type the following in the package root directory:
$ pytest
Contribute
Please read our contribution guidelines.
1 maintainer
Metadata
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Release files for bayes-traj 1.1.7
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