Maintainer: | Patrick Mair, Yves Rosseel, Kathrin Gruber |
Contact: | mair at fas.harvard.edu |
Version: | 2023-12-15 |
URL: | https://CRAN.R-project.org/view=Psychometrics |
Source: | https://github.com/cran-task-views/Psychometrics/ |
Contributions: | Suggestions and improvements for this task view are very welcome and can be made through issues or pull requests on GitHub or via e-mail to the maintainer address. For further details see the Contributing guide. |
Citation: | Patrick Mair, Yves Rosseel, Kathrin Gruber (2023). CRAN Task View: Psychometric Models and Methods. Version 2023-12-15. URL https://CRAN.R-project.org/view=Psychometrics. |
Installation: | The packages from this task view can be installed automatically using the ctv package. For example, ctv::install.views("Psychometrics", coreOnly = TRUE) installs all the core packages or ctv::update.views("Psychometrics") installs all packages that are not yet installed and up-to-date. See the CRAN Task View Initiative for more details. |
Psychometrics is concerned with theory and techniques of psychological measurement. Psychometricians have also worked collaboratively with those in the field of statistics and quantitative methods to develop improved ways to organize, analyze, and scale corresponding data. Since much functionality is already contained in base R and there is considerable overlap between tools for psychometry and tools described in other views, we only give a brief overview of packages that are closely related to psychometric methodology.
Contributions are always welcome and encouraged, either via e-mail to the maintainer or by submitting an issue or pull request in the GitHub repository linked above.
corresp()
and mca()
in package MASS.made4
(see also here ).factanal()
and fa()
and fa.poly()
(ordinal data) in psych.fa.parallel()
and VSS()
for estimating the appropriate number of factors/components as well as ICLUST()
for item clustering.prcomp()
(based on svd()
, preferred) as well as princomp()
(based on eigen()
for compatibility with S-PLUS). Additional rotation methods for FA based on gradient projection algorithms can be found in the package GPArotation. The package nFactors produces a non-graphical solution to the Cattell scree test. Some graphical PCA representations can be found in the psy package. paran implements Horn’s test of principal components/factors.cmdscale()
function. Sammon mapping sammon()
and non-metric MDS isoMDS()
are other relevant functions.metaMDS()
in vegan. Furthermore, labdsv and ecodist provide the function nmds()
. Also, the ExPosition implements a function for metric MDS.capscale()
in vegan; in labdsv and ecodist using pco()
and with dudi.pco()
in ade4.lca()
. Another package is poLCA for polytomous variable latent class analysis. LCA can also be fitted using flexmix which optionally allows for the inclusion of concomitant variables and latent class regression.lme4
.