Integrative Meta-Regression Framework for Descriptive Epidemmiology
This project is the descriptive epidemiological meta-regression tool, DisMod-MR, which grew out of the Global Burden of Disease (GBD) Study 2010. DisMod-MR has been developed for the Institute of Health Metrics and Evaluation at the University of Washington from 2008-2013.
Dismod MR requires PyMC2 which does not play nicely with normal Python installation tools. Fortunately, conda has solved this issue for us. So first you’ll need to setup a conda environment (after installing conda, if necessary) and install pymc. Then you can install dismod_mr using pip.
conda create --name=dismod_mr python=3.6 pymc conda activate dismod_mr pip install dismod_mr
If you get an error stating
ERROR: Complete output from command python setup.py egg_info: ERROR: Traceback (most recent call last): File "<string>", line 1, in <module> File "/tmp/pip-install-d9fbq7v3/pymc/setup.py", line 8, in <module> from numpy.distutils.misc_util import Configuration ModuleNotFoundError: No module named 'numpy' ---------------------------------------- ERROR: Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-install-d9fbq7v3/pymc/
or something similar, you do not have pymc properly installed.
If you want to install dismod_mr locally in an editable mode, the instructions are very similar. We’ll clone the repository and install it from a local directory instead of using pip to grab it from the Python package index.
conda create --name=dismod_mr python=3.6 pymc conda activate dismod_mr git clone firstname.lastname@example.org:ihmeuw/dismod_mr.git cd dismod_mr pip install -e .
- Write tests before code
- Write equations before tests
- Test quantitatively with simulation data
- Test qualitatively with real data
- Automate tests
- Use a package instead of DIY
- Test the package
- Optimize code later
- Optimize code for readability before speed
- .py files should be short, less than 500 lines
- Functions should be short, less than 25 lines
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