{"id":13416140,"url":"https://github.com/inuyasha2012/pypsy","last_synced_at":"2025-03-14T23:31:20.930Z","repository":{"id":41243438,"uuid":"101648541","full_name":"inuyasha2012/pypsy","owner":"inuyasha2012","description":"psychometrics package, including  MIRT(multidimension item response theory), IRT(item response theory),GRM(grade response theory),CAT(computerized adaptive testing), CDM(cognitive diagnostic model), FA(factor analysis), SEM(Structural Equation Modeling) .","archived":false,"fork":false,"pushed_at":"2018-11-16T10:32:53.000Z","size":194,"stargazers_count":215,"open_issues_count":3,"forks_count":71,"subscribers_count":19,"default_branch":"master","last_synced_at":"2024-10-02T07:39:22.970Z","etag":null,"topics":["classical-test-theory","cognitive-diagnostic-models","computerized-adaptive-testing","education","factor-analysis","item-response-theory","psychology","psychometrics","questionnaire","structural-equation-modeling","survey"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/inuyasha2012.png","metadata":{"files":{"readme":"README.rst","changelog":"HISTORY.rst","contributing":"CONTRIBUTING.rst","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-08-28T13:56:59.000Z","updated_at":"2024-09-17T12:13:45.000Z","dependencies_parsed_at":"2022-09-16T20:51:02.943Z","dependency_job_id":null,"html_url":"https://github.com/inuyasha2012/pypsy","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/inuyasha2012%2Fpypsy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/inuyasha2012%2Fpypsy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/inuyasha2012%2Fpypsy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/inuyasha2012%2Fpypsy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/inuyasha2012","download_url":"https://codeload.github.com/inuyasha2012/pypsy/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243663487,"owners_count":20327299,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["classical-test-theory","cognitive-diagnostic-models","computerized-adaptive-testing","education","factor-analysis","item-response-theory","psychology","psychometrics","questionnaire","structural-equation-modeling","survey"],"created_at":"2024-07-30T21:00:54.704Z","updated_at":"2025-03-14T23:31:20.335Z","avatar_url":"https://github.com/inuyasha2012.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":".. image:: https://img.shields.io/travis/inuyasha2012/pypsy.svg\n        :target: https://travis-ci.org/inuyasha2012/pypsy\n\n.. image:: https://coveralls.io/repos/github/inuyasha2012/pypsy/badge.svg?branch=master\n        :target: https://coveralls.io/github/inuyasha2012/pypsy?branch=master\n\n.. image:: https://img.shields.io/pypi/v/psy.svg\n        :target: https://pypi.python.org/pypi/psy\n\n.. image:: https://readthedocs.org/projects/python-psychometrics/badge/?version=latest\n        :target: https://python-psychometrics.readthedocs.io/en/latest/?badge=latest\n\npypsy\n=====\n\n`中文 \u003c./README_ZH.rst\u003e`_\n\npsychometrics package, including structural equation model, confirmatory\nfactor analysis, unidimensional item response theory, multidimensional\nitem response theory, cognitive diagnosis model, factor analysis and\nadaptive testing. The package is still a doll. will be finished in\nfuture.\n\nunidimensional item response theory\n-----------------------------------\n\nmodels\n~~~~~~\n\n-  binary response data IRT (two parameters, three parameters).\n\n-  grade respone data IRT (GRM model)\n\nParameter estimation algorithm\n------------------------------\n\n-  EM algorithm (2PL, GRM)\n\n-  MCMC algorithm (3PL）\n\n--------------\n\nMultidimensional item response theory (full information item factor analysis)\n-----------------------------------------------------------------------------\n\nParameter estimation algorithm\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe initial value\n^^^^^^^^^^^^^^^^^\n\nThe approximate polychoric correlation is calculated, and the slope\ninitial value is obtained by factor analysis of the polychoric\ncorrelation matrix.\n\nEM algorithm\n^^^^^^^^^^^^\n\n-  E step uses GH integral.\n\n-  M step uses Newton algorithm (sparse matrix is divided into non\n   sparse matrix).\n\nFactor rotation\n^^^^^^^^^^^^^^^\n\nGradient projection algorithm\n\nThe shortcomings\n~~~~~~~~~~~~~~~~\n\nGH integrals can only estimate low dimensional parameters.\n\n--------------\n\nCognitive diagnosis model\n-------------------------\n\nmodels\n~~~~~~\n\n-  Dina\n\n-  ho-dina\n\nparameter estimation algorithms\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n-  EM algorithm\n\n-  MCMC algorithm\n\n-  maximum likelihood estimation (only for estimating skill parameters\n   of subjects)\n\n--------------\n\nStructural equation model\n-------------------------\n\n-  contains three parameter estimation methods(ULS, ML and GLS).\n\n-  based on gradient descent\n\n--------------\n\nConfirmatory factor analysis\n----------------------------\n\n-  can be used for continuous data, binary data and ordered data.\n\n-  based on gradient descent\n\n-  binary and ordered data based on Polychoric correlation matrix.\n\n--------------\n\nFactor analysis\n---------------\n\nFor the time being, only for the calculation of full information item\nfactor analysis, it is very simple.\n\nThe algorithm\n~~~~~~~~~~~~~\n\nprincipal component analysis\n\nThe rotation algorithm\n~~~~~~~~~~~~~~~~~~~~~~\n\ngradient projection\n\n--------------\n\nAdaptive test\n-------------\n\nmodel\n~~~~~\n\nThurston IRT model (multidimensional item response theory model for\npersonality test)\n\nAlgorithm\n~~~~~~~~~\n\nMaximum information method for multidimensional item response theory\n\n--------------\n\nRequire\n-------\n\n-  numpy\n\n-  progressbar2\n\n--------------\n\nHow to use it\n-------------\n\ninstall\n~~~~~~~\n::\n\n    pip install psy\n\nSee demo\n\nTODO LIST\n---------\n\n-  theta parameterization of CCFA\n\n-  parameter estimation of structural equation models for multivariate\n   data\n\n-  Bayesin knowledge tracing (Bayesian knowledge tracking)\n\n-  multidimensional item response theory (full information item factor\n   analysis)\n\n-  high dimensional computing algorithm (adaptive integral, etc.)\n\n-  various item response models\n\n-  cognitive diagnosis model\n\n-  G-DINA model\n\n-  Q matrix correlation algorithm\n\n-  Factor analysis\n\n-  maximum likelihood estimation\n\n-  various factor rotation algorithms\n\n-  adaptive\n\n-  adaptive cognitive diagnosis\n\n-  other adaption model\n\n-  standard error and P value\n\n-  code annotation, testing and documentation.\n\nReference\n---------\n\n-  `DINA Model and Parameter Estimation: A\n   Didactic \u003chttp://www.stat.cmu.edu/~brian/PIER-methods/For%202013-03-04/Readings/de%20la%20Torre-dina-est-115-30-jebs.pdf\u003e`__\n-  `Higher-order latent trait models for cognitive\n   diagnosis \u003chttp://www.aliquote.org/pub/delatorre2004.pdf\u003e`__\n-  `Full-Information Item Factor\n   Analysis. \u003chttp://conservancy.umn.edu/bitstream/11299/104282/1/v12n3p261.pdf\u003e`__\n-  `Multidimensional adaptive\n   testing \u003chttp://media.metrik.de/uploads/incoming/pub/Literatur/1996_Multidimensional%20adaptive%20testing.pdf\u003e`__\n-  `Derivative free gradient projection algorithms for rotation \u003chttps://cloudfront.escholarship.org/dist/prd/content/qt9938p4wc/qt9938p4wc.pdf\u003e`__\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finuyasha2012%2Fpypsy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Finuyasha2012%2Fpypsy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finuyasha2012%2Fpypsy/lists"}