Describe the bug In [1]: from statsmodels.datasets import grunfeld In [2]: from statsmodels.api import OLS In [3]: data = grunfeld.load_pandas().data.set_index(['firm','year']) In [4]: mod = OLS(data.invest, data[['value','capital']]) In...
I had a statsmodels.stats.robust_descriptive in work (before I got distracted with release problems), that focuses on robust measures for skew and kurtosis, but could/should also get robust standard deviation. (barely related: a collection of measures comparing two arrays http://statsmodels.source...
Here, we estimated them separately by a maximum likelihood estimation with the statsmodels package28 or using a heuristic method. To estimate the library size factor lc for each cell c, by default, we used the ratio of its total sum count to the average of reference cells. To estimate the...
it has three key control parameters:penaltysize,orderrandomandsampleShareinit. There are some other standard parameters like the maximum execution time, the maximum number of iterations, the pseudo-random number generator seed, etc. The first key parameter quantifies the importance of solution express...
self.data_in_cache.extend(['null'])# robust covariancefromstatsmodels.base.covtypeimportget_robustcov_resultsifuse_tisNone: self.use_t =False#TODO:class defaultelse: self.use_t = use_tifcov_type =='nonrobust': self.cov_type ='nonrobust'self.cov_kwds = {'description':'Standard E...
The libraries used included scikit-learn (1.0.1), imblearn (0.8.1), featurewiz (0.1.7), scipy (1.8.0), scikit_posthocs (0.7.0), statsmodels (0.13.2), and bioinfokit (2.0.8). The feature sets used, scaling methods, unsupervised learning, and supervised classifiers were tailored to ...