Mixed Conditional Logit Models

Specification and estimation of conditional logit models of binary responses and multinomial counts is provided, with or without alternative- specific 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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0.5.1 by Martin Elff, 9 months 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