ivmodels 0.9.0

0.9.0 - 2025-09-04

• Implemented the more powerful critical values of Londschien (2025) for the conditional_likelihood_ratio_test.

ivmodels 0.8.0

0.8.0 - 2025-08-17

New features:

• The functions proj and oproj now accept pandas DataFrames and Series as arguments.
• The conditional_likelihood_ratio_test now supports testing hypotheses for included exogenous covariates.
• The more powerful critical values of Guggenberger et al. (2019) ("gkm") are now supported when testing hypotheses for included exogenous covariates.

Other changes:

• The named_coefs_ attribute of KClass is now renamed to named_coef_. Accessing named_coefs_ will raise a
  DeprecationWarning.

• Quadric.__format__ now prints the same as ConfidenceSet.__format__ if the quadric is one-dimensional.

• Made utils._characteristic_roots more robust when using singular b.

• utils._find_roots now uses scipy.optimize.brentq.

ivmodels 0.7.0

0.7.0 - 2025-07-03

New features:

• The inverse_anderson_rubin_test now supports the GKM critical values by passing critical_values="gkm". These can be used by supplying test="anderson-rubin (gkm)" to Summary or KClass.summary.

Bug fixes:

• Make _characteristic_roots more robust if B is singular.

ivmodels 0.6.0

0.6.0 - 2025-05-22

New features:

• Added the residual_prediction_test for model misspecification.

ivmodels 0.5.3

0.5.3 - 2025-04-21

Bug fixes:

• The classes KClass and AnchorRegression now set attributes n_features_in_ and feature_names_in_ to comply with sckit-learn SLEP 7 and 10.

ivmodels 0.5.2

0.5.2 - 2024-10-03

Bug fixes:

• The Summary now correctly includes the rank and J test results.

ivmodels 0.5.1

0.5.1 - 2024-09-16

Bug fixes:

• Fixed the setuptools configuration.

ivmodels 0.5.0

0.5.0 - 2024-08-27

New features:

• The Wald test now supports robust covariance estimation.

• New method length for ConfidenceSet.

Other changes:

• One can now pass the tolerance parameter tol to the optimization algorithm in
  lagrange_multiplier_test and inverse_lagrange_multiplier_test via the kwargs.

• KClass now raises if kappa >= 1 (as for the LIML and TSLS estimators) and the number of instruments is less than the number of
  endogenous regressors.

• The Summary now only includes and prints the results of the J-statistic and (multivariate) F-test for instrument strength if this makes sense.

• The docs have been updated and include examples.

ivmodels 0.4.0

0.4.0 - 2024-08-08

New features:

• New test j_test of the overidentifying restrictions.

• The tests inverse_lagrange_multiplier_test and inverse_conditional_likelihood_ratio_test now possibly return unions of intervals, instead of one large conservative interval.

Bug fixes:

• Fixed bug in KClass.fit when C is not None and M_{[Z, C]} X is not full rank.

• Fixed bug ininverse_conditional_likelihood_ratio_test when k == mw + mx and C is not None.

• Fixed bug in utils._characteristic_roots if b == np.array([[0]]). This now correctly returns np.inf.

Other changes:

• The Summary now additionally reports the LIML variant of the J-statistic.

ivmodels 0.3.1

Bug fixes:

• Fixed bug in inverse_conditional_likelihood_ratio_test.