independent_response : <anonymous>: possible error in
  isolation.forest(ind_var_occ, ntrees = model$params$ntrees,
  sample_size = model$params$sample_size, ndim = 1, ntry =
  model$params$ntry, max_depth = model$params$max_depth,
  prob_pick_avg_gain = model$params$prob_pick_avg_gain,
  prob_pick_pooled_gain = model$params$prob_pick_pooled_gain,
  prob_split_avg_gain = model$params$prob_split_avg_gain,
  prob_split_pooled_gain = model$params$prob_split_pooled_gain,
  min_gain = model$params$min_gain, missing_action =
  model$params$missing_action, categ_split_type =
  model$params$categ_split_type, : unused arguments
  (prob_split_avg_gain = model$params$prob_split_avg_gain,
  prob_split_pooled_gain = model$params$prob_split_pooled_gain)
independent_response : <anonymous>: possible error in all_perm =
  model$params$all_perm, coef_by_prop = model$params$coef_by_prop,
  weights_as_sample_prob = model$params$weights_as_sample_prob,
  sample_with_replacement = model$params$sample_with_replacement,
  penalize_range = model$params$penalize_range, weigh_by_kurtosis =
  model$params$weigh_by_kurtosis, coefs = model$params$coefs,
  assume_full_distr = model$params$assume_full_distr, build_imputer =
  model$params$build_imputer, min_imp_obs = model$params$min_imp_obs,
  depth_imp = model$params$depth_imp, : unused arguments
  (prob_split_avg_gain = model$params$prob_split_avg_gain,
  prob_split_pooled_gain = model$params$prob_split_pooled_gain)
independent_response : <anonymous>: possible error in weigh_imp_rows =
  model$params$weigh_imp_rows): unused arguments (prob_split_avg_gain =
  model$params$prob_split_avg_gain, prob_split_pooled_gain =
  model$params$prob_split_pooled_gain)
spatial_response : <anonymous>: possible error in
  isolation.forest(ind_var_occ, ntrees = model$params$ntrees,
  sample_size = model$params$sample_size, ndim = 1, ntry =
  model$params$ntry, max_depth = model$params$max_depth,
  prob_pick_avg_gain = model$params$prob_pick_avg_gain,
  prob_pick_pooled_gain = model$params$prob_pick_pooled_gain,
  prob_split_avg_gain = model$params$prob_split_avg_gain,
  prob_split_pooled_gain = model$params$prob_split_pooled_gain,
  min_gain = model$params$min_gain, missing_action =
  model$params$missing_action, categ_split_type =
  model$params$categ_split_type, : unused arguments
  (prob_split_avg_gain = model$params$prob_split_avg_gain,
  prob_split_pooled_gain = model$params$prob_split_pooled_gain)
spatial_response : <anonymous>: possible error in all_perm =
  model$params$all_perm, coef_by_prop = model$params$coef_by_prop,
  weights_as_sample_prob = model$params$weights_as_sample_prob,
  sample_with_replacement = model$params$sample_with_replacement,
  penalize_range = model$params$penalize_range, weigh_by_kurtosis =
  model$params$weigh_by_kurtosis, coefs = model$params$coefs,
  assume_full_distr = model$params$assume_full_distr, build_imputer =
  model$params$build_imputer, min_imp_obs = model$params$min_imp_obs,
  depth_imp = model$params$depth_imp, : unused arguments
  (prob_split_avg_gain = model$params$prob_split_avg_gain,
  prob_split_pooled_gain = model$params$prob_split_pooled_gain)
spatial_response : <anonymous>: possible error in weigh_imp_rows =
  model$params$weigh_imp_rows): unused arguments (prob_split_avg_gain =
  model$params$prob_split_avg_gain, prob_split_pooled_gain =
  model$params$prob_split_pooled_gain)
variable_analysis : <anonymous>: possible error in
  isolation.forest(this_var_occ, ntrees = model$params$ntrees,
  sample_size = model$params$sample_size, ndim = 1, ntry =
  model$params$ntry, max_depth = model$params$max_depth,
  prob_pick_avg_gain = model$params$prob_pick_avg_gain,
  prob_pick_pooled_gain = model$params$prob_pick_pooled_gain,
  prob_split_avg_gain = model$params$prob_split_avg_gain,
  prob_split_pooled_gain = model$params$prob_split_pooled_gain,
  min_gain = model$params$min_gain, missing_action =
  model$params$missing_action, categ_split_type =
  model$params$categ_split_type, : unused arguments
  (prob_split_avg_gain = model$params$prob_split_avg_gain,
  prob_split_pooled_gain = model$params$prob_split_pooled_gain)
variable_analysis : <anonymous>: possible error in all_perm =
  model$params$all_perm, coef_by_prop = model$params$coef_by_prop,
  weights_as_sample_prob = model$params$weights_as_sample_prob,
  sample_with_replacement = model$params$sample_with_replacement,
  penalize_range = model$params$penalize_range, weigh_by_kurtosis =
  model$params$weigh_by_kurtosis, coefs = model$params$coefs,
  assume_full_distr = model$params$assume_full_distr, build_imputer =
  model$params$build_imputer, min_imp_obs = model$params$min_imp_obs,
  depth_imp = model$params$depth_imp, : unused arguments
  (prob_split_avg_gain = model$params$prob_split_avg_gain,
  prob_split_pooled_gain = model$params$prob_split_pooled_gain)
variable_analysis : <anonymous>: possible error in weigh_imp_rows =
  model$params$weigh_imp_rows): unused arguments (prob_split_avg_gain =
  model$params$prob_split_avg_gain, prob_split_pooled_gain =
  model$params$prob_split_pooled_gain)
variable_analysis : <anonymous>: possible error in
  isolation.forest(except_var_occ, ntrees = model$params$ntrees,
  sample_size = model$params$sample_size, ndim =
  ifelse(ncol(except_var_occ) >= model$params$ndim, model$params$ndim,
  ncol(except_var_occ)), ntry = model$params$ntry, max_depth =
  model$params$max_depth, prob_pick_avg_gain =
  model$params$prob_pick_avg_gain, prob_pick_pooled_gain =
  model$params$prob_pick_pooled_gain, prob_split_avg_gain =
  model$params$prob_split_avg_gain, prob_split_pooled_gain =
  model$params$prob_split_pooled_gain, min_gain =
  model$params$min_gain, : unused arguments (prob_split_avg_gain =
  model$params$prob_split_avg_gain, prob_split_pooled_gain =
  model$params$prob_split_pooled_gain)
variable_analysis : <anonymous>: possible error in missing_action =
  model$params$missing_action, categ_split_type =
  model$params$categ_split_type, all_perm = model$params$all_perm,
  coef_by_prop = model$params$coef_by_prop, weights_as_sample_prob =
  model$params$weights_as_sample_prob, sample_with_replacement =
  model$params$sample_with_replacement, penalize_range =
  model$params$penalize_range, weigh_by_kurtosis =
  model$params$weigh_by_kurtosis, coefs = model$params$coefs,
  assume_full_distr = model$params$assume_full_distr, build_imputer =
  model$params$build_imputer, : unused arguments (prob_split_avg_gain =
  model$params$prob_split_avg_gain, prob_split_pooled_gain =
  model$params$prob_split_pooled_gain)
variable_analysis : <anonymous>: possible error in min_imp_obs =
  model$params$min_imp_obs, depth_imp = model$params$depth_imp,
  weigh_imp_rows = model$params$weigh_imp_rows): unused arguments
  (prob_split_avg_gain = model$params$prob_split_avg_gain,
  prob_split_pooled_gain = model$params$prob_split_pooled_gain)