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Bumps statsmodels from 0.10.2 to 0.12.0.

Release notes

Sourced from statsmodels's releases.

Release 0.12.0

The statsmodels developers are happy to announce release 0.12.0. 239 issues were closed in this release and 221 pull requests were merged.

Major new features include:

  • New exponential smoothing model: ETS (Error, Trend, Seasonal)
  • New dynamic factor model for large datasets and monthly/quarterly mixed frequency models
  • Decomposition of forecast updates based on the "news"
  • Sparse Cholesky Simulation Smoother
  • Option to use Chandrasekhar recursions
  • Two popular methods for forecasting time series, forecasting after STL decomposition and the Theta model
  • Functions for constructing complex Deterministic Terms in time series models
  • New statistics function: one-way ANOVA-type tests, hypothesis tests for 2-samples and meta-analysis.

Release Candidate 0.12.0rc0

The statsmodels developers are happy to announce the first release candidate for 0.12.0. 223 issues were closed in this release and 208 pull requests were merged. Major new features include:

  • New exponential smoothing model: ETS (Error, Trend, Seasonal)
  • New dynamic factor model for large datasets and monthly/quarterly mixed frequency models
  • Decomposition of forecast updates based on the "news"
  • Sparse Cholesky Simulation Smoother
  • Option to use Chandrasekhar recursions
  • Two popular methods for forecasting time series, forecasting after STL decomposition and the Theta model
  • Functions for constructing complex Deterministic Terms in time series models

Release 0.11.1

This is a bug fix release. It fixes a small number of bugs including two that affect the installation on statmodels on Python 2.7 and 3.8.

See the full release notes (or in rst format) for the full set of backported pull requests.

Release 0.11.0

statsmodels developers are happy to announce a new release.

Major new features include:

  • Regression
    • Rolling OLS and WLS
  • Statistics
    • Oaxaca-Blinder decomposition
    • Distance covariance measures (new in RC2)
    • New regression diagnostic tools (new in RC2)
  • Statespace Models
    • Statespace-based Linear exponential smoothing models¶
    • Methods to apply parameters fitted on one dataset to another dataset¶
    • Method to hold some parameters fixed at known values
    • Option for low memory operations
    • Improved access to state estimates
    • Improved simulation and impulse responses for time-varying models
  • Time-Series Analysis
Commits
  • a424650 Merge pull request #7003 from bashtage/rls-docs
  • 4c5191b DOC: Final doc changes for release
  • b25fb33 DOC: Final doc changes for release
  • 633f80d Merge pull request #7002 from bashtage/dbivolaru-master
  • 85b1829 Merge pull request #7000 from esmucler/mad-center
  • d5748a5 Merge pull request #7001 from josef-pkt/release_notes_stats_012
  • c5668ca MAINT/TST: Clean kde and add more tests
  • ca4e080 ENH: Allow custom bandwidth functions in KDEUnivariate fit
  • b3e76b9 simpler solution
  • dc9d05c DOC: add new stats to release notes for 0.12
  • Additional commits viewable in compare view

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@dependabot-preview dependabot-preview bot added the dependencies Pull requests that update a dependency file label Aug 28, 2020
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codecov bot commented Aug 28, 2020

Codecov Report

Merging #270 into master will increase coverage by 0.03%.
The diff coverage is n/a.

Impacted file tree graph

@@            Coverage Diff             @@
##           master     #270      +/-   ##
==========================================
+ Coverage   82.24%   82.28%   +0.03%     
==========================================
  Files         112      113       +1     
  Lines        9406     9408       +2     
==========================================
+ Hits         7736     7741       +5     
+ Misses       1670     1667       -3     
Flag Coverage Δ
#unittests ?

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Impacted Files Coverage Δ
src/__init__.py 100.00% <0.00%> (ø)
src/evalg/genprog/generators.py 95.83% <0.00%> (+1.04%) ⬆️
src/evalg/genprog/mutation.py 93.93% <0.00%> (+2.02%) ⬆️

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