glmmLasso R Error:Error in grad.lasso[b.is.0]

I am trying to use glmmLasso to run a multilevel logistic regression model, as I believe I am getting some wonky results due to sparse data bias. My outcome variable is a binary (0, 1) variable and my grouping variable is ID. Here is my code:

m1 <- glmmLasso(outcome ~ c.AGE + c.BSS + c.negative.emotion + 
   c.PSI_Total + c.MEPS_Ratio + c.OTT_Ratio + c.AAQ_Total + 
   c.BHS_Total, rnd=list(ID=~1 + c.negative.emotion), 
   lambda=100, data=data.set.3, family=binomial(link="logit")) 

This is the error I am receiving:

Error in grad.lasso[b.is.0] <- score.beta[b.is.0] - lambda.b * sign(score.beta[b.is.0]) : NAs are not allowed in subscripted assignments In addition: Warning message: In Ops.factor(y, Mu) : ‘-’ not meaningful for factors

I am unsure why I am getting this error. I did see another post about this error on StackOverflow but I was unable to use the fix for my data. There are no NAs in the dataset. I've attached the dataset in CSV format here.