Metadata-Version: 1.1
Name: lccm
Version: 0.1.20
Summary: Estimation of latent class choice models using Expectation Maximization algorithm
Home-page: https://github.com/ferasz/LCCM
Author: Feras El Zarwi and Akshay Vij
Author-email: feraselzarwi@gmail.com, vij.akshay@gmail.com
License: BSD License
Description: What Lccm is
        ===============
        Lccm is a Python package for estimating latent class choice models 
        using the Expectation Maximization (EM) algorithm to maximize the likelihood function.
        
        Main Features
        =============
        
        * Latent Class Choice Models
        
        * Supports datasets where the choice set differs across observations.
        * Supports model specifications where the coefficient for a given variable may be generic (same coefficient across all alternatives) or alternative specific (coefficients varying across all alternatives or subsets of alternatives) in each latent class.
        * Accounts for sampling weights in case the data you are working with is choice-based i.e. Weighted Exogenous Sample Maximum Likelihood (WESML) from (Ben-Akiva and Lerman, 1983) to yield consistent estimates.
        * Constrains the choice set across latent classes whereby each latent class can have its own subset of alternatives in the respective choice set.
        * Constrains the availability of latent classes to all individuals in the sample whereby it might be the case that a certain latent class or set of latent classes are unavailable to certain decision-makers.
        
        Where to get it
        ===============
        Available from PyPi::
            pip install lccm
        
            https://pypi.python.org/pypi/lccm/0.1.20
        
        
        For More Information
        ====================
        For more information about the lccm code, see the following dissertation:
            El Zarwi, Feras. "Modeling and Forecasting the Impact of Major Technological and Infrastructural Changes on Travel Demand", PhD Dissertation, 2017, University of California at Berkeley.
        
        Attribution
        ===========
        If Lccm is useful in your research or work, please cite this package by citing the dissertation above and the package itself.
        
        License
        =======
        Modified BSD (3-clause)
        
        Changelog
        =========
        
        0.1.20 (April 21st, 2017)
        -------------------------
        - Initial package release for estimating latent class choice models using the Expectation Maximization Algorithm.
        
Keywords: latent class choice models Expectation Maximization algorithm discrete choice modeling econometrics
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
