{
    "info": {
        "author": "chris dai",
        "author_email": "inuyasha021@163.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
        ],
        "description": ".. image:: https://img.shields.io/travis/inuyasha2012/pypsy.svg\r\n        :target: https://travis-ci.org/inuyasha2012/pypsy\r\n\r\n.. image:: https://coveralls.io/repos/github/inuyasha2012/pypsy/badge.svg?branch=master\r\n        :target: https://coveralls.io/github/inuyasha2012/pypsy?branch=master\r\n\r\npypsy\r\n=====\r\n\r\n`\u4e2d\u6587 <./README_ZH.rst>`_\r\n\r\n`DINA Model and Parameter Estimation: A\r\n   Didactic <http://www.stat.cmu.edu/~brian/PIER-methods/For%202013-03-04/Readings/de%20la%20Torre-dina-est-115-30-jebs.pdf>`\r\n\r\npsychometrics package, including structural equation model, confirmatory\r\nfactor analysis, unidimensional item response theory, multidimensional\r\nitem response theory, cognitive diagnosis model, factor analysis and\r\nadaptive testing. The package is still a doll. will be finished in\r\nfuture.\r\n\r\nunidimensional item response theory\r\n-----------------------------------\r\n\r\nmodels\r\n~~~~~~\r\n\r\n-  binary response data IRT (two parameters, three parameters).\r\n\r\n-  grade respone data IRT (GRM model)\r\n\r\nParameter estimation algorithm\r\n------------------------------\r\n\r\n-  EM algorithm (2PL, GRM)\r\n\r\n-  MCMC algorithm (3PL\uff09\r\n\r\n--------------\r\n\r\nMultidimensional item response theory (full information item factor analysis)\r\n-----------------------------------------------------------------------------\r\n\r\nParameter estimation algorithm\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n\r\nThe initial value\r\n^^^^^^^^^^^^^^^^^\r\n\r\nThe approximate polychoric correlation is calculated, and the slope\r\ninitial value is obtained by factor analysis of the polychoric\r\ncorrelation matrix.\r\n\r\nEM algorithm\r\n^^^^^^^^^^^^\r\n\r\n-  E step uses GH integral.\r\n\r\n-  M step uses Newton algorithm (sparse matrix is divided into non\r\n   sparse matrix).\r\n\r\nFactor rotation\r\n^^^^^^^^^^^^^^^\r\n\r\nGradient projection algorithm\r\n\r\nThe shortcomings\r\n~~~~~~~~~~~~~~~~\r\n\r\nGH integrals can only estimate low dimensional parameters.\r\n\r\n--------------\r\n\r\nCognitive diagnosis model\r\n-------------------------\r\n\r\nmodels\r\n~~~~~~\r\n\r\n-  Dina\r\n\r\n-  ho-dina\r\n\r\nparameter estimation algorithms\r\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\r\n\r\n-  EM algorithm\r\n\r\n-  MCMC algorithm\r\n\r\n-  maximum likelihood estimation (only for estimating skill parameters\r\n   of subjects)\r\n\r\n--------------\r\n\r\nStructural equation model\r\n-------------------------\r\n\r\n-  contains three parameter estimation methods(ULS, ML and GLS).\r\n\r\n-  based on gradient descent\r\n\r\n--------------\r\n\r\nConfirmatory factor analysis\r\n----------------------------\r\n\r\n-  can be used for continuous data, binary data and ordered data.\r\n\r\n-  based on gradient descent\r\n\r\n-  binary and ordered data based on Polychoric correlation matrix.\r\n\r\n--------------\r\n\r\nFactor analysis\r\n---------------\r\n\r\nFor the time being, only for the calculation of full information item\r\nfactor analysis, it is very simple.\r\n\r\nThe algorithm\r\n~~~~~~~~~~~~~\r\n\r\nprincipal component analysis\r\n\r\nThe rotation algorithm\r\n~~~~~~~~~~~~~~~~~~~~~~\r\n\r\ngradient projection\r\n\r\n--------------\r\n\r\nAdaptive test\r\n-------------\r\n\r\nmodel\r\n~~~~~\r\n\r\nThurston IRT model (multidimensional item response theory model for\r\npersonality test)\r\n\r\nAlgorithm\r\n~~~~~~~~~\r\n\r\nMaximum information method for multidimensional item response theory\r\n\r\nRequire\r\n-------\r\n\r\n-  numpy\r\n\r\n-  progressbar2\r\n\r\nHow to use it\r\n-------------\r\n\r\nSee demo in detail\r\n\r\nTODO LIST\r\n---------\r\n\r\n-  theta parameterization of CCFA\r\n\r\n-  parameter estimation of structural equation models for multivariate\r\n   data\r\n\r\n-  Bayesin knowledge tracing (Bayesian knowledge tracking)\r\n\r\n-  multidimensional item response theory (full information item factor\r\n   analysis)\r\n\r\n-  high dimensional computing algorithm (adaptive integral, etc.)\r\n\r\n-  various item response models\r\n\r\n-  cognitive diagnosis model\r\n\r\n-  G-DINA model\r\n\r\n-  Q matrix correlation algorithm\r\n\r\n-  Factor analysis\r\n\r\n-  maximum likelihood estimation\r\n\r\n-  various factor rotation algorithms\r\n\r\n-  adaptive\r\n\r\n-  adaptive cognitive diagnosis\r\n\r\n-  other adaption model\r\n\r\n-  standard error and P value\r\n\r\n-  code annotation, testing and documentation.\r\n\r\nReference\r\n---------\r\n\r\n-  `DINA Model and Parameter Estimation: A\r\n   Didactic <http://www.stat.cmu.edu/~brian/PIER-methods/For%202013-03-04/Readings/de%20la%20Torre-dina-est-115-30-jebs.pdf>`__\r\n-  `Higher-order latent trait models for cognitive\r\n   diagnosis <http://www.aliquote.org/pub/delatorre2004.pdf>`__\r\n-  `Full-Information Item Factor\r\n   Analysis. <http://conservancy.umn.edu/bitstream/11299/104282/1/v12n3p261.pdf>`__\r\n-  `Multidimensional adaptive\r\n   testing <http://media.metrik.de/uploads/incoming/pub/Literatur/1996_Multidimensional%20adaptive%20testing.pdf>`__\r\n-  `Derivative free gradient projection algorithms for rotation <https://cloudfront.escholarship.org/dist/prd/content/qt9938p4wc/qt9938p4wc.pdf>`__\r\n\r\n\r\n=======\r\nHistory\r\n=======\r\n\r\n0.0.1 (2018-09-18)\r\n------------------\r\n\r\n* First release on PyPI.\r\n\r\n\r\n",
        "description_content_type": "",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "https://github.com/inuyasha2012/pypsy",
        "keywords": "psy",
        "license": "MIT license",
        "maintainer": "",
        "maintainer_email": "",
        "name": "psy",
        "package_url": "https://pypi.org/project/psy/",
        "platform": "",
        "project_url": "https://pypi.org/project/psy/",
        "project_urls": {
            "Homepage": "https://github.com/inuyasha2012/pypsy"
        },
        "release_url": "https://pypi.org/project/psy/0.0.1/",
        "requires_dist": [
            "numpy",
            "progressbar2",
            "scipy"
        ],
        "requires_python": "",
        "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.",
        "version": "0.0.1"
    },
    "last_serial": 4287300,
    "releases": {
        "0.0.1": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "98757a3dc11c3f85d3cd66a4fe3bdd00",
                    "sha256": "167c116fb312993f36d3f988e97fff69aa61e261156e800f2e1447e7e7276ab8"
                },
                "downloads": -1,
                "filename": "psy-0.0.1-py2.py3-none-any.whl",
                "has_sig": false,
                "md5_digest": "98757a3dc11c3f85d3cd66a4fe3bdd00",
                "packagetype": "bdist_wheel",
                "python_version": "py2.py3",
                "requires_python": null,
                "size": 38321,
                "upload_time": "2018-09-19T07:04:27",
                "url": "https://files.pythonhosted.org/packages/ef/45/2b97f1c60c0d3e5c233911246140229771f62a9760b7f0774d104d34e2b3/psy-0.0.1-py2.py3-none-any.whl"
            },
            {
                "comment_text": "",
                "digests": {
                    "md5": "22325ac3a9fb81b04315bc4f853bfd66",
                    "sha256": "bb674edc63a661b7f3e0447c56b978883dd01e805eb33ffb06238c2f8ee70455"
                },
                "downloads": -1,
                "filename": "psy-0.0.1.tar.gz",
                "has_sig": false,
                "md5_digest": "22325ac3a9fb81b04315bc4f853bfd66",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 71966,
                "upload_time": "2018-09-19T07:04:29",
                "url": "https://files.pythonhosted.org/packages/91/bd/7bafe2a4b8c176743c1a926272c13f77bd9c0ff839653c5f9697deb03557/psy-0.0.1.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "98757a3dc11c3f85d3cd66a4fe3bdd00",
                "sha256": "167c116fb312993f36d3f988e97fff69aa61e261156e800f2e1447e7e7276ab8"
            },
            "downloads": -1,
            "filename": "psy-0.0.1-py2.py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "98757a3dc11c3f85d3cd66a4fe3bdd00",
            "packagetype": "bdist_wheel",
            "python_version": "py2.py3",
            "requires_python": null,
            "size": 38321,
            "upload_time": "2018-09-19T07:04:27",
            "url": "https://files.pythonhosted.org/packages/ef/45/2b97f1c60c0d3e5c233911246140229771f62a9760b7f0774d104d34e2b3/psy-0.0.1-py2.py3-none-any.whl"
        },
        {
            "comment_text": "",
            "digests": {
                "md5": "22325ac3a9fb81b04315bc4f853bfd66",
                "sha256": "bb674edc63a661b7f3e0447c56b978883dd01e805eb33ffb06238c2f8ee70455"
            },
            "downloads": -1,
            "filename": "psy-0.0.1.tar.gz",
            "has_sig": false,
            "md5_digest": "22325ac3a9fb81b04315bc4f853bfd66",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 71966,
            "upload_time": "2018-09-19T07:04:29",
            "url": "https://files.pythonhosted.org/packages/91/bd/7bafe2a4b8c176743c1a926272c13f77bd9c0ff839653c5f9697deb03557/psy-0.0.1.tar.gz"
        }
    ]
}