Easiest to Use Mixed Effects Multinomial Package

Question

I'm trying to analyze a dataset where there are 3 different categorical outcomes, so I think I need to do a multinomial logit regression, but I want to be able to include the test subject as a random effect since I did repeated measures for each subject (albeit 3 of the 20 subjects I only have 1 replicate for, which might also be problematic). I have looked at other threads, including this one.

Most straightforward R package for setting subject as random effect in mixed logit model

However, I don't think the answer to this thread is straightforward either and I've tried other packages which either give me error messages or are hard to interpret.

I tried following the mixcat code (see link below), but I don't know how to interpret the output of it (i.e., if my model coefficients are significant or not)

https://stats.stackexchange.com/questions/492998/multinomial-glmm-with-glmmadmb-in-r

I tried the mgcv::gam code as well.

Anyway, the only reason I want to do a mixed effects model is to account for repeated measures among my test subjects, and that's it. Any suggestions (ideally with a similar syntax to using glm or lmer) would be greatly appreciated. Thanks!

install.packages("mixcat")
library(mixcat)

lionfish_data$Lionfish_ID <- as.factor(lionfish_data$Lionfish_ID)
lionfish_data$Init_pref <- as.factor(lionfish_data$Init_pref)

attach(lionfish_data)

model.po <- npmlt(formula = Init_pref ~ inv_Lionfish_wt_g + Prey_wt_ratio, 
              formula.npo = ~ 1, 
              id = Lionfish_ID, 
              k = 2)

summary(model.po)


detach(lionfish_data)