{"id":32206729,"url":"https://github.com/jansteinfeld/tmt","last_synced_at":"2026-02-23T05:02:18.554Z","repository":{"id":56934205,"uuid":"176006148","full_name":"jansteinfeld/tmt","owner":"jansteinfeld","description":"tmt package for the application of the conditional maximum likelihood (CML) estimation in multistage 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returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["cml","irt","multistage","rasch-model"],"created_at":"2025-10-22T05:35:00.999Z","updated_at":"2026-02-23T05:02:18.548Z","avatar_url":"https://github.com/jansteinfeld.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# tmt \u003cimg src=\"man/figures/tmt.png\" width=\"120\" align=\"right\" alt=\"\"/\u003e\n\n\u003c!-- README.md is generated from README.Rmd--\u003e\n\n[![R-CMD-check](https://github.com/jansteinfeld/tmt/actions/workflows/check-full.yaml/badge.svg)](https://github.com/jansteinfeld/tmt/actions/workflows/check-full.yaml)\n[![GitHub\ntest-coverage](https://github.com/jansteinfeld/tmt/actions/workflows/test-coverage.yaml/badge.svg)](https://github.com/jansteinfeld/tmt/actions/workflows/test-coverage.yaml)\n[![GitHub\npages-build](https://github.com/jansteinfeld/tmt/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/jansteinfeld/tmt/actions/workflows/pages/pages-build-deployment)\n[![GitHub\nversion](https://img.shields.io/github/r-package/v/jansteinfeld/tmt?label=version\u0026logo=github)](https://github.com/jansteinfeld/tmt/)\n[![GitHub\nrelease](https://img.shields.io/github/v/release/jansteinfeld/tmt?label=release\u0026logo=github)](https://github.com/jansteinfeld/tmt/)\n[![GitHub pull\nrequests](https://img.shields.io/github/issues-pr/jansteinfeld/tmt?label=pull%20requests\u0026logo=github)](https://github.com/jansteinfeld/tmt/pulls)\n[![GitHub\nissues](https://img.shields.io/github/issues-raw/jansteinfeld/tmt?label=issues\u0026logo=github)](https://github.com/jansteinfeld/tmt/issues)\n\n[![codecov](https://codecov.io/gh/jansteinfeld/tmt/branch/master/graph/badge.svg?token=11lw4stBoI)](https://app.codecov.io/gh/jansteinfeld/tmt)\n[![CRAN\nversion](https://img.shields.io/cran/v/tmt?label=CRAN%20version)](https://cran.r-project.org/package=tmt)\n[![CRAN\nchecks](https://badges.cranchecks.info/summary/tmt.svg)](https://cran.r-project.org/web/checks/check_results_tmt.html)\n[![downloads](http://cranlogs.r-pkg.org/badges/last-month/tmt?color=blue)](https://cran.r-project.org/package=tmt)\n[![lifecycle](https://img.shields.io/badge/lifecycle-experimental-blue.svg)](https://github.com/jansteinfeld/tmt)\n[![License](https://img.shields.io/cran/l/tmt)](https://opensource.org/license/GPL-3.0)\n\nThe *tmt* Package provides conditional maximum likelihood (CML) item\nparameter estimation of sequential as well as cumulative deterministic\nmultistage (MST) designs (Zwitser \u0026 Maris, 2015,\n[\\\u003c10.1007/s11336-013-9369-6\\\u003e](https://doi.org/10.1007/s11336-013-9369-6))\nas well as probabilistic sequential and cumulative multistage designs\n(Steinfeld \u0026 Robitzsch, 2021,\n[\\\u003c10.31234/osf.io/ew27f\\\u003e](https://doi.org/10.31234/osf.io/ew27f)).\nSupports CML item parameter estimation of conventional linear designs\nand additional functions for the likelihood ratio test (Andersen, 1973,\n[\\\u003c10.1007/BF02291180\\\u003e](https://doi.org/10.1007/BF02291180)) as well as\nfunctions for the simulation of several kinds of multistage designs.\n\n## Installation\n\nTo install the latest (development) version of the *tmt* package, please\ncopy the following commands in your R console:\n\n``` r\n# Install release version from CRAN\ninstall.packages(\"tmt\")\n# Install development version from GitHub\ndevtools::install_github(\"jansteinfeld/tmt\")\n```\n\n## Usage\n\nThe application of the *tmt* package is illustrated below. Further\nexamples of different MST designs can be found in the associated\nvignette of the package. To apply the package and the CML method, it is\nfirst necessary to specify the MST design. For this purpose, a model\nlanguage has been developed, which is illustrated in the first part of\nthe example below. First, each module of the design needs to be\nspecified. The following deterministic sequential MST design consists of\nsix modules, four paths and three stages. In the first part, the modules\nof the MST design are defined (basically the allocation of items).\nDifferent methods are available, the user can either use the R function\n*paste*, but also address the elements manually as vectors. It is\nimportant that the names of the specified elements in the modules match\nthose in the data. To illustrate the application, some data is then\nsimulated based on the specified MST design. In this example, a seed has\nbeen set to make the results easier to compare and follow. The *tmt_rm*\nfunction is available for the actual estimation of the item parameters.\nIf the data has been generated with the *tmt_sim* function, it would be\nsufficient to export the data generated with this function as part of\nthe MST design. If the data has not been generated synthetically with\nthis function, it is necessary to specify the MST design.\n\nA detailed description of the package (such as sequential cumulative and\nprobabilistic MST designs) can be found in the vignette.\n\n``` r\nlibrary(tmt)\n\n# spezification of the mst design\nmstdesign \u003c- \"\n    M1 =~ paste0('i',1:5)\n    M2 =~ c(i6, i7, i8, i9, i10)\n    M3 =~ c(i11, i12, i13, i14, i15)\n    M4 =~ c(i16, i17, i18, i19, i20)\n    M5 =~ c(i21, i22, i23, i24, i25)\n    M6 =~ c(i26, i27, i28, i29, i30)\n\n    # define branches\n    p1 := M4(0,2) + M2(0,2) + M1(0,5)\n    p2 := M4(0,2) + M2(3,5) + M3(0,5)\n    p3 := M4(3,5) + M5(0,2) + M3(0,5)\n    p4 := M4(3,5) + M5(3,5) + M6(0,5)\n  \"\n\n# application of the simulation function to generate som synthetic data\n  items \u003c- seq(-2,2,length.out=30)\n  names(items) \u003c- paste0(\"i\",1:30)\n  \n  dat_mst \u003c- tmt_sim(mstdesign = mstdesign,\n        items = items,\n        persons = 500,\n        seed = 1111)\n\n# estimate the item parameters\nmod1 \u003c- tmt_rm(dat_mst, mstdesign = mstdesign)\n\n\nsummary(mod1)\n#\u003e \n#\u003e Call:\n#\u003e   tmt_rm(dat = dat_mst, mstdesign = mstdesign)\n#\u003e \n#\u003e \n#\u003e Results of Rasch model (mst) estimation: \n#\u003e \n#\u003e Difficulty parameters: \n#\u003e              est.b_i1   est.b_i2   est.b_i3   est.b_i4   est.b_i5   est.b_i6   est.b_i7   est.b_i8\n#\u003e Estimate   -2.3490510 -1.7664751 -2.1028057 -1.9872254 -1.4511516 -1.3097642 -1.2542426 -0.8697938\n#\u003e Std. Error  0.2700265  0.2498593  0.2594456  0.2555346  0.2456593  0.1556009  0.1546331  0.1491392\n#\u003e              est.b_i9  est.b_i10  est.b_i11  est.b_i12  est.b_i13   est.b_i14  est.b_i15\n#\u003e Estimate   -0.7865043 -0.6227596 -0.6582138 -0.2153395 -0.2016450 -0.07909168 0.06922466\n#\u003e Std. Error  0.1482092  0.1466760  0.1346944  0.1297456  0.1296568  0.12903128 0.12868102\n#\u003e             est.b_i16 est.b_i17 est.b_i18 est.b_i19 est.b_i20 est.b_i21 est.b_i22 est.b_i23\n#\u003e Estimate   0.04382588 0.2171609 0.2750574 0.5483552 0.5681985 0.6245467 0.9624785 1.0910584\n#\u003e Std. Error 0.11184169 0.1120871 0.1122291 0.1132990 0.1134044 0.1663841 0.1683083 0.1695429\n#\u003e            est.b_i24 est.b_i25 est.b_i26 est.b_i27 est.b_i28 est.b_i29 est.b_i30\n#\u003e Estimate   1.2884075 1.5650529 1.4674082 1.5247222 1.5824928 2.0063785  1.819695\n#\u003e Std. Error 0.1719177 0.1763681 0.2582108 0.2588762 0.2596804 0.2698234  0.264414\n#\u003e \n#\u003e CLL: -3179.989 \n#\u003e Number of iterations: 60 \n#\u003e Number of parameters: 30\n```\n\n### Outlook\n\nThe following features are planned for future releases:\n\n- the partial credit model for multistage designs\n- missing values in multistage designs\n- improving the speed of the package\n- plots of the multistage design\n\n## References\n\n- Glas, C. A. W. (1988). The Rasch Model and Multistage Testing.\n  *Journal of Educational Statistics, 13*(1), 45. doi: 10.2307/1164950\n- Steinfeld, J., \u0026 Robitzsch, A. (2024). Conditional maximum likelihood\n  estimation in probability-based multistage designs. *Behaviormetrika,\n  51*(2), 617-634.\n- Steinfeld, J., Robitzsch, A. (2023). Estimating item parameters in\n  multistage designs with the tmt package in R. *Quantitative and\n  Computational Methods in Behavioral Science, 3*, e10087.\n  \u003chttps://doi.org/10.5964/qcmb.10087\u003e\n- Steinfeld, J., \u0026 Robitzsch, A. (2021). Item parameter estimation in\n  multistage designs: A comparison of different estimation approaches\n  for the Rasch model. *Psych, 3*(3), 279-307.\n  \u003chttps://doi.org/10.3390/psych3030022\u003e\n- Zwitser, R. J., \u0026 Maris, G. (2013). Conditional statistical inference\n  with multistage testing designs. *Psychometrika, 80*(1), 65-84. doi:\n  10.1007/s11336-013-9369-6\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjansteinfeld%2Ftmt","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjansteinfeld%2Ftmt","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjansteinfeld%2Ftmt/lists"}