Package: OptSig 2.2

OptSig: Optimal Level of Significance for Regression and Other Statistical Tests

The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.

Authors:Jae H. Kim <[email protected]>

OptSig_2.2.tar.gz
OptSig_2.2.zip(r-4.5)OptSig_2.2.zip(r-4.4)OptSig_2.2.zip(r-4.3)
OptSig_2.2.tgz(r-4.4-any)OptSig_2.2.tgz(r-4.3-any)
OptSig_2.2.tar.gz(r-4.5-noble)OptSig_2.2.tar.gz(r-4.4-noble)
OptSig_2.2.tgz(r-4.4-emscripten)OptSig_2.2.tgz(r-4.3-emscripten)
OptSig.pdf |OptSig.html✨
OptSig/json (API)

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16 exports 0.00 score 1 dependencies 20 scripts 192 downloads

Last updated 2 years agofrom:2fbf7b2e65. Checks:OK: 7. Indexed: yes.

Exports:Opt.sig.norm.testOpt.sig.t.testOptSig.2pOptSig.2p2nOptSig.anovaOptSig.BootOptSig.BootWeightOptSig.ChisqOptSig.FOptSig.pOptSig.rOptSig.t2nOptSig.WeightPower.ChisqPower.FR.OLS

Dependencies:pwr