Package: aMNLFA 1.1.2
aMNLFA: Automated Moderated Nonlinear Factor Analysis Using 'M-plus'
Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) <doi:10.1016/j.addbeh.2018.10.031>. This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment.
Authors:
aMNLFA_1.1.2.tar.gz
aMNLFA_1.1.2.zip(r-4.5)aMNLFA_1.1.2.zip(r-4.4)aMNLFA_1.1.2.zip(r-4.3)
aMNLFA_1.1.2.tgz(r-4.4-any)aMNLFA_1.1.2.tgz(r-4.3-any)
aMNLFA_1.1.2.tar.gz(r-4.5-noble)aMNLFA_1.1.2.tar.gz(r-4.4-noble)
aMNLFA_1.1.2.tgz(r-4.4-emscripten)aMNLFA_1.1.2.tgz(r-4.3-emscripten)
aMNLFA.pdf |aMNLFA.html✨
aMNLFA/json (API)
# Install 'aMNLFA' in R: |
install.packages('aMNLFA', repos = c('https://vtcole.r-universe.dev', 'https://cloud.r-project.org')) |
- xstudy - Simulated cross-study data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 3 years agofrom:66abdfd654. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 29 2024 |
R-4.5-win | NOTE | Oct 29 2024 |
R-4.5-linux | NOTE | Oct 29 2024 |
R-4.4-win | NOTE | Oct 29 2024 |
R-4.4-mac | NOTE | Oct 29 2024 |
R-4.3-win | NOTE | Oct 29 2024 |
R-4.3-mac | NOTE | Oct 29 2024 |
Exports:aMNLFA.DIFplotaMNLFA.finalaMNLFA.initialaMNLFA.itemplotsaMNLFA.objectaMNLFA.pruneaMNLFA.sampleaMNLFA.scoresaMNLFA.simultaneousfixPathwrite.inp.file
Dependencies:askpassbackportsbase64encbootbrewbriobslibcachemcallrcheckmateclicliprcodacolorspacecommonmarkcpp11crayoncredentialscurldata.tabledescdevtoolsdiffobjdigestdownlitdplyrellipsisevaluatefansifarverfastDummiesfastmapfontawesomefsgenericsgertggplot2ghgitcredsgluegridExtragsubfngtablehighrhtmltoolshtmlwidgetshttpuvhttrhttr2iniisobandjquerylibjsonliteknitrlabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimeminiUIMplusAutomationmunsellnlmeopensslpanderpillarpkgbuildpkgconfigpkgdownpkgloadplyrpraiseprettyunitsprocessxprofvispromisesprotopspurrrR6raggrappdirsrcmdcheckRColorBrewerRcpprematch2remotesreshape2rlangrmarkdownroxygen2rprojrootrstudioapirversionssassscalessessioninfoshinysourcetoolsstringistringrsyssystemfontstestthattexregtextshapingtibbletidyselecttinytexurlcheckerusethisutf8vctrsviridisLitewaldowhiskerwithrxfunxml2xopenxtableyamlzip
Readme and manuals
Help Manual
Help page | Topics |
---|---|
aMNLFA plotting function for aMNLFA.prune() results | aMNLFA.DIFplot |
aMNLFA simultaneous model fitting function | aMNLFA.final |
aMNLFA initial model fitting function | aMNLFA.initial |
aMNLFA item plotting function | aMNLFA.itemplots |
aMNLFA object function | aMNLFA.object |
aMNLFA simultaneous model fitting function | aMNLFA.prune |
aMNLFA sampling function | aMNLFA.sample |
aMNLFA score generating function | aMNLFA.scores |
aMNLFA simultaneous model fitting function | aMNLFA.simultaneous |
helper function - removes the final slash at the end of a given string | fixPath |
helper function for writing out Mplus inputs | write.inp.file |
Simulated cross-study data | xstudy |