Metadata-Version: 1.1
Name: psy
Version: 0.0.1
Summary: psychometrics package, including structural equation model, confirmatory factor analysis, unidimensional item response theory, multidimensional item response theory, cognitive diagnosis model, factor analysis and adaptive testing. 
Home-page: https://github.com/inuyasha2012/pypsy
Author: chris dai
Author-email: inuyasha021@163.com
License: MIT license
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        pypsy
        =====
        
        `中文 <./README_ZH.rst>`_
        
        `DINA Model and Parameter Estimation: A
           Didactic <http://www.stat.cmu.edu/~brian/PIER-methods/For%202013-03-04/Readings/de%20la%20Torre-dina-est-115-30-jebs.pdf>`
        
        psychometrics package, including structural equation model, confirmatory
        factor analysis, unidimensional item response theory, multidimensional
        item response theory, cognitive diagnosis model, factor analysis and
        adaptive testing. The package is still a doll. will be finished in
        future.
        
        unidimensional item response theory
        -----------------------------------
        
        models
        ~~~~~~
        
        -  binary response data IRT (two parameters, three parameters).
        
        -  grade respone data IRT (GRM model)
        
        Parameter estimation algorithm
        ------------------------------
        
        -  EM algorithm (2PL, GRM)
        
        -  MCMC algorithm (3PL）
        
        --------------
        
        Multidimensional item response theory (full information item factor analysis)
        -----------------------------------------------------------------------------
        
        Parameter estimation algorithm
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The initial value
        ^^^^^^^^^^^^^^^^^
        
        The approximate polychoric correlation is calculated, and the slope
        initial value is obtained by factor analysis of the polychoric
        correlation matrix.
        
        EM algorithm
        ^^^^^^^^^^^^
        
        -  E step uses GH integral.
        
        -  M step uses Newton algorithm (sparse matrix is divided into non
           sparse matrix).
        
        Factor rotation
        ^^^^^^^^^^^^^^^
        
        Gradient projection algorithm
        
        The shortcomings
        ~~~~~~~~~~~~~~~~
        
        GH integrals can only estimate low dimensional parameters.
        
        --------------
        
        Cognitive diagnosis model
        -------------------------
        
        models
        ~~~~~~
        
        -  Dina
        
        -  ho-dina
        
        parameter estimation algorithms
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        -  EM algorithm
        
        -  MCMC algorithm
        
        -  maximum likelihood estimation (only for estimating skill parameters
           of subjects)
        
        --------------
        
        Structural equation model
        -------------------------
        
        -  contains three parameter estimation methods(ULS, ML and GLS).
        
        -  based on gradient descent
        
        --------------
        
        Confirmatory factor analysis
        ----------------------------
        
        -  can be used for continuous data, binary data and ordered data.
        
        -  based on gradient descent
        
        -  binary and ordered data based on Polychoric correlation matrix.
        
        --------------
        
        Factor analysis
        ---------------
        
        For the time being, only for the calculation of full information item
        factor analysis, it is very simple.
        
        The algorithm
        ~~~~~~~~~~~~~
        
        principal component analysis
        
        The rotation algorithm
        ~~~~~~~~~~~~~~~~~~~~~~
        
        gradient projection
        
        --------------
        
        Adaptive test
        -------------
        
        model
        ~~~~~
        
        Thurston IRT model (multidimensional item response theory model for
        personality test)
        
        Algorithm
        ~~~~~~~~~
        
        Maximum information method for multidimensional item response theory
        
        Require
        -------
        
        -  numpy
        
        -  progressbar2
        
        How to use it
        -------------
        
        See demo in detail
        
        TODO LIST
        ---------
        
        -  theta parameterization of CCFA
        
        -  parameter estimation of structural equation models for multivariate
           data
        
        -  Bayesin knowledge tracing (Bayesian knowledge tracking)
        
        -  multidimensional item response theory (full information item factor
           analysis)
        
        -  high dimensional computing algorithm (adaptive integral, etc.)
        
        -  various item response models
        
        -  cognitive diagnosis model
        
        -  G-DINA model
        
        -  Q matrix correlation algorithm
        
        -  Factor analysis
        
        -  maximum likelihood estimation
        
        -  various factor rotation algorithms
        
        -  adaptive
        
        -  adaptive cognitive diagnosis
        
        -  other adaption model
        
        -  standard error and P value
        
        -  code annotation, testing and documentation.
        
        Reference
        ---------
        
        -  `DINA Model and Parameter Estimation: A
           Didactic <http://www.stat.cmu.edu/~brian/PIER-methods/For%202013-03-04/Readings/de%20la%20Torre-dina-est-115-30-jebs.pdf>`__
        -  `Higher-order latent trait models for cognitive
           diagnosis <http://www.aliquote.org/pub/delatorre2004.pdf>`__
        -  `Full-Information Item Factor
           Analysis. <http://conservancy.umn.edu/bitstream/11299/104282/1/v12n3p261.pdf>`__
        -  `Multidimensional adaptive
           testing <http://media.metrik.de/uploads/incoming/pub/Literatur/1996_Multidimensional%20adaptive%20testing.pdf>`__
        -  `Derivative free gradient projection algorithms for rotation <https://cloudfront.escholarship.org/dist/prd/content/qt9938p4wc/qt9938p4wc.pdf>`__
        
        
        =======
        History
        =======
        
        0.0.1 (2018-09-18)
        ------------------
        
        * First release on PyPI.
        
Keywords: psy
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
