"pca" metrics about performance HOT 6 CLOSED

This would definitely fit.

Other things that come to my mind when I think of the scope:

• Other indices used in the structural equation field (this bunch of guys). As they are computed by default by lavaan, it would mostly consist of extractors consistent with the easyverse.
• Convenience methods for PCAs / Factor Analysis, returning the % of variance explained.
• I think that in general, and in the future, the easyverse (that is a thing now :) methods could be useful in the machine learning world, where people struggle with different models/packages. Providing a unifying syntax for extracting, interpreting and understanding their models could be quite appreciated. Although this will probably wait for the help of a future contributor, expert in this kind of things. But providing the tools to bridge the regression world with the ML world could be quite cool.

from performance.

According the 2nd point, I think we can use the code from here:
https://strengejacke.github.io/sjstats/reference/pca.html

from performance.

I've also made a parameters extraction implementation for fa/pca and lavaan in psycho, and I didn't know sjstats had it also (I guess it does too many things 😛)...

from performance.

I guess it does too many things

Yes, that's why an "out-sourcing" into separate packages makes sense. ;-) In the long term, I'll remove those functions from sjstats and just keep a set of useful functions that do not exactly fit into existing easyverse-packages.

from performance.

What do think about the name? Currently, we have pca() and pca_rotate(). What about item_pca(), to be more consistent with this "group" of functions?

from performance.

What about integrating the two (similar to psych::pca()) so that the rotation is an argument of the former, which would lower the number of exported functions.

As for the name, pca is good because it is widely known by this name. However, I think the name would clash with psych, which principal() seems to have pca as an alias.

We could go full plain with principal_components()? or principal_components_analysis (as you said, we live in the time of auto-completion ^^)?

principal_components make sense tho as the function returns precisely the principal components. Whereas a principal components analysis would also possibly refer to other pre/post processing steps.

from performance.