Changelog

v0.11.0 (planned)

  • Standardised the recurrent-model API on the same instance-based fitters the univariate distributions use. Every recurrent fitter (HPP, CrowAMSAA, Duane, CoxLewis, NonParametricCounting, the renewal fitters GeneralizedRenewal/GeneralizedOneRenewal/ARA/ARI and the proportional-intensity fitters) is now a configured singleton instance with an instance-method fit() rather than a class with a @classmethod fit. Public Model.fit(...) calls are unchanged. Internally this is provided by the new surpyval.utils.fitter.singleton_fitter decorator. Also removed the unused ParametricRecurrenceRegressionModel stub.

  • General ALT fitter full release

  • General PH fitter full release

  • Formulas

  • Add more than Breslow to the CoxPH methods.

  • Parameter confidence bound

  • Document the rationale behind using Fleming-Harrington as the default.

  • Docs on how to integrate with Pandas

  • Docs for CoxPH

  • Docs for Accelerated Life fitters

  • Create a RegressionFitter class. I keep copying code across the three fitters.

  • Allow truncation with zi and lfp models.

  • Allow truncation with regression

v0.10.1.0 (25 Mar 2022)

  • Changed plot methods to now take ‘Axis’ object. This allows a user to pass in an existing axis.

  • plot functions now return an Axis object instead of the Lines2D object. Allows for easy user update after plotting.

  • Added fs_to_xcn as it was dropped in 10.0.1.

  • Changed all imports for numpy to be done from the surpyval module. This will allow for easy maintenance in future in the event of deprecated autograd.

v0.10.0.1 (22 Nov 2021)

  • Removed fsl_to_xcn function and replaced with fsli_to_xcn function that is able to take any combination of fsli.

v0.10.0 (9 Aug 2021)

  • Version snapshot for JOSS review

v0.9.0 (5 Aug 2021)

  • Better initial estimates in the _parameter_initialiser for the lfp data (use max F from nonp estimate…)

  • issue #13 - Better failures when insufficient data provided.

  • issue #12 - Created fsli_to_xcn helper function.

  • Fixed bug in confidence bounds implementation for offset distributions. CBs were not using the offset and were therefore way out. Now fixed.

  • Created a NonParametric.cb() method to match Parametric API for confidence bounds.

  • Cleaned up NonParametric code (removed some technical debt and duplicated code).

  • Changed the __repr__ function in NonParametric to be aligned to Parametric

  • Updated the docstring for fit() for NonParametric

  • Fixed bug in NonParametric that required the x input to be in order for the functions (e.g. df etc.).

  • CoxPH released.

  • General AL fitter in beta

  • General PH fitter in beta

  • Created Linear, Power, InversePower, Exponential, InverseExponential, Eyring, InverseEyring, DualPower, PowerExponential, DualExponential life models.

  • Created GeneralLogLinear life model for variable stress count input.

  • For each combination of a SurPyval distribution and life model, there is an instance to use fit(). For example there are WeibullDualExponential, LogNormalPower, ExponentialExponential etc.

  • Docs Updates:
    • Add application examples to docs:

      • Reliability Engineering

      • Actuary / Demography

      • Social Science/Criminology

      • Boston Housing

      • Medical science

      • Economics

      • Biology - Ware, J.H., Demets, D.L.: Reanalysis of some baboon descent data. Biometrics 459–463 (1976).

v0.8.0 (27 July 2021)

  • Made backwards incompatible changes to LFP models, these are now created with the lfp=True keyword in the fit() method

  • Created ability to fit zero-inflated models. Simply pass the zi=True option to the fit() method.

  • Chanages to utils.xcnt_handler to ensure x, xl, and xr are handled consistently.

  • changed the way __repr__ displays a Parametric object.

  • Changed the default for plotting to be Fleming-Harrington. This was a result of seeing how poorly the Nelson-Aalen method fits zero inflated models. FH therefore offers the best performance of a Non-Parametric estimate at the low values of the survival function (as KM reaches 0 for fully observed data) and at high values (KM is good but NA is poor).

  • Added a Fleming-Harrington method to the Turnbull class.

  • Improved stability with dedicated log_sf, log_ff, and log_df functions. Less chance of overflows and therefore better convergence.

  • Changed interpolation method of NonParametric. Allows for use of cubic interpolation

  • Changed from_params to accept lfp and zi (or any combo)

  • Changed random() in Parametric so that lfp or zi models can be simulated!

  • Improved the way surpyval fails

  • Substantial docs updates.

v0.7.0 (19 July 2021)

  • Major changes to the confidence bounds for Parametric models. Now use the cb() method for every bound.

  • Removed the OffsetParametric class and made Parametric class now work with (or without) an offset.

  • Minor doc updates.