In R, `Error in f(arg, ...) : NA/NaN/Inf in foreign function call (arg 1)` but there are no Infs, no NaNs, no `char`s, etc - r

Question

I am trying to use the lqmm package in R and receiving the error Error in f(arg, ...) : NA/NaN/Inf in foreign function call (arg 1). I can successfully use it for a version of my data in which a variable called cluster_name is averaged over.
I've tried to verify that there are no NaNs or infinite values in my dataset this way:
na_data = mydata
new_DF <- na_data[rowSums(is.na(mydata)) > 0,] # yields a dataframe with no observations
is.na(na_data) <- sapply(na_data, is.infinite)
new_DF <- na_data[rowSums(is.na(mydata)) > 0,] # still a dataframe with no observations
There are no variables in my dataframe that are type char -- every such variable has been converted to a factor.
When I run my model
m1 = lqmm(std_brain ~ std_beh*type*taught, random = ~1, group=subject, data = begin_data, tau=.5, na.action=na.exclude)
on the first 12,528 lines of my dataset, the model works fine. Line 12,529 looks totally normal.
Similarly, if I run tail(mydata, 11943) I get a dataframe that runs without error, but tail(mydata, 11944) gives me a dataframe that generates the error. I can also run a subset from 9990:21825 without error, but extending the dataframe on either side generates the error. The whole dataframe is 29450 observations, and thus this middle slice contains the supposedly problematic observations. I tried making a smaller version of my dataset that contained just the borders of problems, and some observations around them, and I can see that 3/4 cases involve the same subject (7645), but I don't know what to make of that. I don't see how to make this reproducible without providing the whole dataframe (in case you were wondering, the small dataset doesn't cause any error). So here is the csv file I used.
Here is the function that gets the dataframe ready for analysis:
prep_data_set <- function(data_file, brain_var = 'beta', beh_var = 'accuracy') {
data = read.csv(data_file)
data$subject <- factor(data$subject)
data$type <- factor(data$type)
data$type <- relevel(data$type, ref = "S")
data$taught <- factor(data$taught)
data <- subset(data, data$run_num < 13)
data$run = factor(data$run_num)
brain_mean <- mean(data[[brain_var]])
brain_sd <- sd(data[[brain_var]])
beh_mean <- mean(data[[beh_var]])
beh_sd <- sd(data[[beh_var]])
data <- subset(data, data$cluster_name != "")
data$cluster_name <- factor(data$cluster_name)
data$mean_centered_brain <- data[[brain_var]]
data$std_brain <- data$mean_centered_brain/brain_sd
data$mean_centered_beh <- data[[beh_var]]
data$std_beh <- data$mean_centered_beh/beh_sd
return(data)
}
I run
mydata = prep_data_set(file.path(resdir, 'robust0005', 'pos_rel_con__all_clusters.csv'))
m1 = lqmm(std_brain ~ std_beh*type*taught, random = ~1, group=subject, data = mydata, tau=.5, na.action=na.exclude)
to generate the error.
By comparison
regular_model = lmer(std_brain ~ type*taught*std_beh + (1|subject/run) +
(1|subject:cluster_name), data = mydata)
runs fine.
I hope there is something interesting and generalizable in this question; I know it's kind of annoying to post to Stack Overflow with some idiosyncratic problem in a ~30000 line dataset.