{ "_id": "6a227b61cd65a98ecbd4e758", "Package": "tcl", "Type": "Package", "Title": "Testing in Conditional Likelihood Context", "Version": "1.0.1", "Date": "2025-10-05", "Authors@R": "c(person(given = \"Clemens\",\nfamily = \"Draxler\",\nrole = c(\"aut\", \"cre\"),\nemail = \"clemens.draxler@umit-tirol.at\"),\nperson(given = \"Andreas\",\nfamily = \"Kurz\",\nrole = \"aut\",\nemail = \"andreas.kurz@plus.ac.at\"))", "Author": "Clemens Draxler [aut, cre], Andreas Kurz [aut]", "Maintainer": "Clemens Draxler ", "Description": "An implementation of hypothesis testing in an extended\nRasch modeling framework, including sample size planning\nprocedures and power computations. Provides 4 statistical\ntests, i.e., gradient test (GR), likelihood ratio test (LR),\nRao score or Lagrange multiplier test (RS), and Wald test, for\ntesting a number of hypotheses referring to the Rasch model\n(RM), linear logistic test model (LLTM), rating scale model\n(RSM), and partial credit model (PCM). Three types of functions\nfor power and sample size computations are provided. Firstly,\nfunctions to compute the sample size given a user-specified\n(predetermined) deviation from the hypothesis to be tested, the\nlevel alpha, and the power of the test. Secondly, functions to\nevaluate the power of the tests given a user-specified\n(predetermined) deviation from the hypothesis to be tested, the\nlevel alpha of the test, and the sample size. Thirdly,\nfunctions to evaluate the so-called post hoc power of the\ntests. This is the power of the tests given the observed\ndeviation of the data from the hypothesis to be tested and a\nuser-specified level alpha of the test. Power and sample size\ncomputations are based on a Monte Carlo simulation approach. It\nis computationally very efficient. The variance of the random\nerror in computing power and sample size arising from the\nsimulation approach is analytically derived by using the delta\nmethod. Additionally, functions to compute the power of the\ntests as a function of an effect measure interpreted as\nexplained variance are provided. Draxler, C., & Alexandrowicz,\nR. W. 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