Fix some lints

R/complete_levels.R

@@ -33,7 +33,7 @@ complete_levels <- function(x, character_levels = NULL) {
         # HACK: Some models use a character variable with many levels (e.g.,
         # mixed-effects groups). This creates a massive padding dataset, and making
         # predictions can become very expensive.
-        if (isTRUE(sum(sapply(vault, length)) > 100)) return(data.frame())
+        if (isTRUE(sum(lengths(vault)) > 100)) return(data.frame())
 
         padding <- utils::head(x, 1)
         data.table::setDT(padding)

R/datagrid.R

@@ -296,7 +296,7 @@ datagrid_engine <- function(
     out <- lapply(out, unique)
 
     # warn on very large prediction grid
-    num <- as.numeric(sapply(out, length)) # avoid integer overflow
+    num <- as.numeric(lengths(out)) # avoid integer overflow
     num <- Reduce(f = "*", num)
     if (isTRUE(num > 1e9)) {
         stop("You are trying to create a prediction grid with more than 1 billion rows, which is likely to exceed the memory and computational power available on your local machine. Presumably this is because you are considering many variables with many levels. All of the functions in the `marginaleffects` package include arguments to specify a restricted list of variables over which to create a prediction grid.", call. = FALSE)

R/get_se_delta.R

@@ -64,7 +64,7 @@ get_se_delta <- function(model,
 
     # delta method does not work for these models
     bad <- c("brmsfit", "stanreg", "bart")
-    if (any(bad %in% class(model))) {
+    if (any(inherits(model, bad))) {
         return(NULL)
     }
 

R/get_term_labels.R

@@ -1,6 +1,6 @@
 get_term_labels <- function(x, idx = NULL) {
   if (is.data.frame(x)) {
-    if ("term" %in% names(x) && length(unique(x$term)) == nrow(x)) {
+    if ("term" %in% names(x) && anyDuplicated(x$term) == 0L) {
       return(unique(x$term))
     } else if (any(grepl("^contrast", names(x)))) {
       tmp <- grep("^term$|^contrast", names(x))
@@ -23,4 +23,4 @@ get_term_labels <- function(x, idx = NULL) {
   }
   if (!is.null(idx)) out <- out[idx]
   return(out)
-}
+}

R/methods_aod.R

@@ -12,7 +12,7 @@ get_predict.glimML <- function(model,
                         type = type,
                         ...)
     out <- data.frame(
-        rowid = 1:nrow(newdata),
+        rowid = seq_len(nrow(newdata)),
         estimate = out)
 
     return(out)

R/methods_brms.R

@@ -64,7 +64,7 @@ get_predict.brmsfit <- function(model,
     } else if ("rowid" %in% colnames(newdata)) {
         idx <- newdata[["rowid"]]
     } else {
-        idx <- 1:nrow(newdata)
+        idx <- seq_len(nrow(newdata))
     }
 
     # resp_subset sometimes causes dimension mismatch 

R/methods_crch.R

@@ -33,7 +33,7 @@ get_predict.crch <- function(model,
     sanity_predict_vector(pred = pred, model = model, newdata = newdata, type = type)
     sanity_predict_numeric(pred = pred, model = model, newdata = newdata, type = type)
     out <- data.frame(
-        rowid = 1:nrow(newdata),
+        rowid = seq_len(nrow(newdata)),
         estimate = pred)
     return(out)
 }

R/methods_gamlss.R

@@ -115,7 +115,7 @@ set_coef.gamlss <- function(model, coefs, ...){
               len[is.null(len)] <- 1
               data <- rbind(...)
           } else {
-              len <- sapply(tmp, length)
+              len <- lengths(tmp)
               data <- unlist(tmp)
           }
           namelist <- factor(rep(names, len), levels = names)

R/methods_glmmTMB.R

@@ -21,7 +21,7 @@ get_predict.glmmTMB <- function(model,
 
     np <- model$fit$par
     if (!is.null(newparams)) {
-        np[1:length(newparams)] <- newparams
+        np[seq_along(newparams)] <- newparams
     }
 
     out <- get_predict.default(

R/methods_nnet.R

@@ -83,7 +83,7 @@ get_predict.multinom <- function(model,
     if (is_latent && is_mclogit) {
         missing_level <- as.character(unique(insight::get_response(model)))
         missing_level <- setdiff(missing_level, colnames(pred))
-        if (length(missing_level == 1)) {
+        if (length(missing_level) == 1) {
             pred <- cbind(0, pred)
             colnames(pred)[1] <- missing_level
             pred <- pred - rowMeans(pred)

R/methods_ordinal.R

@@ -36,7 +36,7 @@ get_predict.clm <- function(model,
     if ("rowid" %in% colnames(newdata)) {
         out$rowid <- rep(newdata$rowid, times = ncol(pred))
     } else {
-        out$rowid <- rep(1:nrow(pred), times = ncol(pred))
+        out$rowid <- rep(seq_len(nrow(pred)), times = ncol(pred))
     }
 
     return(out)