handling a lot of variables at the same time and do purely imputation

[YushuShi]

Just have two questions:

1. Can JointAI package do purely imputation, but no modeling?
2. I found it has some difficulty handling a large number of covariates? When I give 50 covariates, it's OK, but when I give more than 100, it says
   Error in vapply(x, fun, FUN.VALUE = character(1L), ..., USE.NAMES = TRUE) :
   values must be length 1,
   but FUN(X[[1]]) result is length 2
   In addition: Warning messages:
   1: In gsub(paste0("^", clean_lhs, "[[ ]]*"), "", deparse(x, width.cutoff = 500L)) :
   argument 'pattern' has length > 1 and only the first element will be used
   2: Using formula(x) is deprecated when x is a character vector of length > 1.
   Consider formula(paste(x, collapse = " ")) instead.
   3: Using formula(x) is deprecated when x is a character vector of length > 1.
   Consider formula(paste(x, collapse = " ")) instead.

Thanks a lot!

[NErler]

1. You can get multiple imputed datasets from JointAI. To do that, you need to specify the argument monitor_params(imps = TRUE). Then you can use the function get_MIdat().
2. Theoretically, there is no limit to the number of covariates you can use, but I didn't anticipate such huge formulas and used deparse with the maximum width.cutoff = 500 in several places. I expect that the issue arises because your formula is longer than that. I'll see what I can do about fixing this.