Mixed Conditional Logit Models

Specification and estimation of conditional logit models of binary responses and multinomial counts is provided, with or without random effects. The current implementation of the estimator for random effects variances uses a Laplace approximation (or PQL) approach and thus should be used only if groups sizes are large.


2014-10-13: Simplified some namespace dependencies. Eliminated useless pseudo-R-squared statistics form getSummary.mclogit

2014-08-23: Added 'anova' methods

2014-03-10: Refactored code -- algorithms should be more transparent and robust now (hopefully!). mclogit without and with random effects can handle missing values now. Fixed predict method -- use of napredict; handles single indep-variable situation now. Fixed embarassing typo -- prior weights do work now (again?). Included AIC and BIC methods contributed by Nic Elliot [email protected]

Reference manual

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0.6 by Martin Elff, a month ago


Report a bug at http://github.com/melff/mclogit/issues

Browse source code at https://github.com/cran/mclogit

Authors: Martin Elff

Documentation:   PDF Manual  

GPL-2 license

Imports memisc, methods

Depends on stats, Matrix

Imported by mztwinreg.

Enhanced by prediction, stargazer.

See at CRAN