v0.18.0
CamDavidsonPilon
released this
01 Feb 01:11
·
1035 commits
to master
since this release
0.18.0
LogNormalFitter
is a new univariate fitter you can use.WeibullFitter
now correctly returns the confidence intervals (previously returned only NaNs)WeibullFitter.print_summary()
displays p-values associated with its parameters not equal to 1.0 - previously this was (implicitly) comparing against 0, which is trivially always true (the parameters must be greater than 0)ExponentialFitter.print_summary()
displays p-values associated with its parameters not equal to 1.0 - previously this was (implicitly) comparing against 0, which is trivially always true (the parameters must be greater than 0)ExponentialFitter.plot
now displays the cumulative hazard, instead of the survival function. This is to make it easier to compare toWeibullFitter
andLogNormalFitter
- Univariate fitters'
cumulative_hazard_at_times
,hazard_at_times
,survival_function_at_times
return pandas Series now (use to be numpy arrays) - remove
alpha
keyword from all statistical functions. This was never being used. - Gone are astericks and dots in
print_summary
functions that represent signficance thresholds. - In models'
summary
(includingprint_summary
), thelog(p)
term has changed to-log2(p)
. This is known as the s-value. See https://lesslikely.com/statistics/s-values/ - introduce new statistical tests between univariate datasets:
survival_difference_at_fixed_point_in_time_test
,... - new warning message when Cox models detects possible non-unique solutions to maximum likelihood.
- Generally: clean up lifelines exception handling. Ex: catch
LinAlgError: Matrix is singular.
and report back to the user advice.