Style fixups
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@ -14,7 +14,7 @@
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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from .bayes_factors import BayesFactor, bayesfactor_afbf
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from .bayes_factors import bayesfactor_afbf
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from .distributions import beta_oddsratio, beta_ratio, hdi, transformed_dist
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from .fs import pickle_read_compressed, pickle_read_encrypted, pickle_write_compressed, pickle_write_encrypted
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from .regress import PenalisedLogit, regress, vif
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@ -15,9 +15,7 @@
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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class BayesFactor:
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"""
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A Bayes factor
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"""
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"""A Bayes factor"""
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def __init__(self, factor, num_symbol, num_desc, denom_symbol, denom_desc):
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self.factor = factor
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@ -284,9 +284,7 @@ transformed_dist = transformed_gen(name='transformed')
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ConfidenceInterval = collections.namedtuple('ConfidenceInterval', ['lower', 'upper'])
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def hdi(distribution, level=0.95):
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"""
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Get the highest density interval for the distribution, e.g. for a Bayesian posterior, the highest posterior density interval (HPD/HDI)
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"""
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"""Get the highest density interval for the distribution, e.g. for a Bayesian posterior, the highest posterior density interval (HPD/HDI)"""
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# For a given lower limit, we can compute the corresponding 95% interval
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def interval_width(lower):
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@ -188,9 +188,7 @@ class PearsonChiSquaredResult:
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self.ct, self.dof, self.statistic, fmt_p(self.pvalue, html=False))
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def chi2(df, dep, ind, *, nan_policy='warn'):
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"""
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Perform a Pearson chi-squared test
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"""
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"""Perform a Pearson chi-squared test"""
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# Check for/clean NaNs
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df = check_nan(df[[ind, dep]], nan_policy)
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