Minor documentation update
- Add a figure parameter to histogram and manhattan plots in order to plot to an existing figure
- SurveyDesignSpec can now utilize more parameters, such as fpc
- The larger (numeric or alphabetic) binary variable is always treated as the success case for binary phenotypes
- Improved logging during EWAS, including printing the survey design information
- Extensively updated documentation
- CLARITE now has a logo!
- Corrected an indexing error that sometimes occurred when removing rows with missing weights
- Improve precision in EWAS results for weighted analyses by using sf instead of 1-cdf
- Change some column names in the EWAS output to be more clear
An R script and the output of that script is now included. The R output is compared to the python output in the test suite in order to ensure analysis result concordance between R and Python for several analysis scenarios.
- Allow file input in the command line for skip/only
- Make the manhattan plot function less restrictive of the data passed into it
- Use skip/only in the transform function
- Categorization would silently fail if there was only one variable of a given type
- Improvements to the CLI and printed log messages.
- The functions from the ‘Process’ module were put into the ‘Modify’ module.
- Datasets are no longer split apart when categorizing.
Extensive changes in organization, but limited new functionality (not counting the CLI).
- Added a function to recode values - https://github.com/HallLab/clarite-python/issues/4
- Added a function to filter outlier values - https://github.com/HallLab/clarite-python/issues/5
- Added a function to generate manhattan plots for multiple datasets together - https://github.com/HallLab/clarite-python/issues/9
- Add some validation of input DataFrames to prevent some errors in calculations
- Added an initial batch of tests
Support EWAS with binary outcomes. Additional handling of NA values in covariates and the phenotype. Add a ‘min_n’ parameter to the ewas function to require a minimum number of observations after removing incomplete cases. Add additional functions including ‘plot_distributions’, ‘merge_variables’, ‘get_correlations’, ‘get_freq_table’, and ‘get_percent_na’
Add support for complex survey designs
Added documentation for existing functions
First functional version. Mutliple methods are available under a ‘clarite’ Pandas accessor.