I'm using v0.1.0.

eqMI.main throws the following error with my data:

Error in findInterval(stat.feed$RMESA_t[i], RMESA_cutoffs[i, 1:4]) : 'vec' must be sorted non-decreasingly and not contain NAs

I've used na.omit() to ensure there are no NAs in the data but the error persists. A google tells me that others have comparable issues with their use of findInterval in other packages, but I don't fully understand it.

Minimal working example here: https://www.dropbox.com/sh/x374y24i4e0syw7/AAAW_VQVFwNcUcC0coRpo1iZa?dl=0

Is it possible to use the eq.MI.main when there's missing data? As of right now I don't think there is because I kept getting the following error:

"Error in (function (..., lamb0 = NULL) :
Check missing data in raw data or NAs in the sample covariances"

hi, first of all thank you for the amazing package
second, is it possible to do hierarchical measurement invariance in equaltestMI?
the regular model works fine but when I'm trying to test the hierarchical one it pops
"Error in [.data.frame(dotdotdot$data, , c(useVar, dotdotdot$group)) : undefined columns selected"

the hierarchical model is goof in lavaan.

thank you
dror

My previous CFAs employ the WLSMV estimator, so my understanding is that it would be logical for equaltestMI to use it too. The documentation for the dots argument for eqMI.main specifies that "The same arguments as for any lavaan model. See lavaan::sem for more information." However, when I run the following code:

eqMI.main(
    model = my_model,
    data = my_data,
    order = c("1", "2", "3", "4", "5", "6"), 
    estimator = "WLSMV",  
    group = group, 
    meanstructure = TRUE,
    output = 'both',
    equivalence.test = FALSE,
    adjRMSEA = TRUE,
    bootstrap = FALSE,
    projection = FALSE,
    quiet = TRUE
)

It throws the following error:

Error in *tmp*[[j]] : subscript out of bounds

Last step in traceback is:

[<-.data.frame(*tmp*, ind[!is.na(ind)], c("cfi.delta", "rmsea.delta"), value = NA)

I've tried multiple datasets, so it doesn't seem specific to strange or extreme data.

Have I misunderstood anything here about either the strategy or implementation?