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.