Package: psy
.. image:: https://img.shields.io/travis/inuyasha2012/pypsy.svg:target: https://travis-ci.org/inuyasha2012/pypsy.. image:: https://coveralls.io/repos/github/inuyasha2012/pypsy/badge.svg?branch=master:target: https://coveralls.io/github/inuyasha2012/pypsy?branch=masterpypsy=====`中文 <./README_ZH.rst>`_`DINA Model and Parameter Estimation: ADidactic <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, confirmatoryfactor analysis, unidimensional item response theory, multidimensionalitem response theory, cognitive diagnosis model, factor analysis andadaptive testing. The package is still a doll. will be finished infuture.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 slopeinitial value is obtained by factor analysis of the polychoriccorrelation matrix.EM algorithm^^^^^^^^^^^^-  E step uses GH integral.-  M step uses Newton algorithm (sparse matrix is divided into nonsparse matrix).Factor rotation^^^^^^^^^^^^^^^Gradient projection algorithmThe shortcomings~~~~~~~~~~~~~~~~GH integrals can only estimate low dimensional parameters.--------------Cognitive diagnosis model-------------------------models~~~~~~-  Dina-  ho-dinaparameter estimation algorithms~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-  EM algorithm-  MCMC algorithm-  maximum likelihood estimation (only for estimating skill parametersof 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 itemfactor analysis, it is very simple.The algorithm~~~~~~~~~~~~~principal component analysisThe rotation algorithm~~~~~~~~~~~~~~~~~~~~~~gradient projection--------------Adaptive test-------------model~~~~~Thurston IRT model (multidimensional item response theory model forpersonality test)Algorithm~~~~~~~~~Maximum information method for multidimensional item response theoryRequire--------  numpy-  progressbar2How to use it-------------See demo in detailTODO LIST----------  theta parameterization of CCFA-  parameter estimation of structural equation models for multivariatedata-  Bayesin knowledge tracing (Bayesian knowledge tracking)-  multidimensional item response theory (full information item factoranalysis)-  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: ADidactic <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 cognitivediagnosis <http://www.aliquote.org/pub/delatorre2004.pdf>`__-  `Full-Information Item FactorAnalysis. <http://conservancy.umn.edu/bitstream/11299/104282/1/v12n3p261.pdf>`__-  `Multidimensional adaptivetesting <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.