Update files

Author: Zülal Bekerecioğlu

DESCRIPTION

@@ -59,7 +59,9 @@ Imports:
     childsds, 
     purrr, 
     tibble,
-    tidyselect
+    tidyselect,
+    stats,
+    lubridate
 Suggests:
     testthat
 RoxygenNote: 7.3.3

R/dateDS.R

@@ -29,7 +29,6 @@
 dateDS <- function(x=NULL, type=NULL,
                    newobj=NULL, unit=NULL, add.column=NULL) { 
 
-  library(lubridate)
   add.column <- as.logical(add.column)
 
   # If argument not in c("extractdate", "makedate", "timebetween") throw an error
@@ -119,9 +118,9 @@ dateDS <- function(x=NULL, type=NULL,
 
     # Extract the requested component
     result <- switch(unit,
-                     days   = day(date_input),
-                     months = month(date_input),
-                     years  = year(date_input),
+                     days   = lubridate::day(date_input),
+                     months = lubridate::month(date_input),
+                     years  = lubridate::year(date_input),
                      stop("Invalid unit. Must be one of: days, months, years"))
   }
 
@@ -141,7 +140,7 @@ dateDS <- function(x=NULL, type=NULL,
     }
 
     if (any(year_vec < 1000 | year_vec > 3000, na.rm = TRUE)) {
-      stop("The 'year' input in 'makedate' must contain plausible 4-digit years (1000–3000).
+      stop("The 'year' input in 'makedate' must contain plausible 4-digit years (1000-3000).
            Check that year, month, and day are given in the correct order (year, month, day).", call. = FALSE)
     }
     if (any(month_vec < 1 | month_vec > 12, na.rm = TRUE)) {
@@ -154,9 +153,9 @@ dateDS <- function(x=NULL, type=NULL,
     }
 
 
-    result <- make_date(year  = year_vec,
-                          month = month_vec,
-                          day   = day_vec)
+    result <- lubridate::make_date(year  = year_vec,
+                                   month = month_vec,
+                                   day   = day_vec)
   }
 
 
@@ -166,12 +165,12 @@ dateDS <- function(x=NULL, type=NULL,
 
     # inputs[[1]] = start date, inputs[[2]] = end date
     units <- list(
-      years  = years(1),
-      months = months(1),
-      days   = days(1)
+      years  = lubridate::years(1),
+      months = lubridate::months(1),
+      days   = lubridate::days(1)
     )
 
-    result <- interval(inputs[[1]], inputs[[2]]) %/% units[[unit]]
+    result <- lubridate::interval(inputs[[1]], inputs[[2]]) %/% units[[unit]]
   }
 
   # Save result based on add.column

R/predictDS.R

@@ -71,15 +71,15 @@ predictDS <- function(newdataname, traindataname, type = c("response", "link"),
 
   # SPECIAL CASE HANDLING: y ~ 1, intercept only ######################
   # A numeric vector will be created wit the mean, with the same length as the row number in newdataname 
-  special_case <- length(attr(terms(formula(model_formula)), "term.labels")) == 0
+  special_case <- length(attr(stats::terms(stats::formula(model_formula)), "term.labels")) == 0
 
   if(special_case){
     intercept <- coefficients
 
     # If the input is just a numeric vector, get the length
-    if(class(newdf)=="numeric"){
+    if(all(c("numeric") %in% class(newdf))){
       predictions.f <- rep(intercept, length(newdf))
-    } else if(class(newdf)=="data.frame"){
+    } else if(all(c("data.frame") %in% class(newdf))){
       predictions.f <- rep(intercept, nrow(newdf)) # Otherwise use the number of rows
     } else {
       stop("Invalid input: The object called 'newdataname' must be either a numeric vector or a data.frame.",, call. = FALSE)
@@ -95,7 +95,7 @@ predictDS <- function(newdataname, traindataname, type = c("response", "link"),
       predictions.f <- exp(predictions.f)     
     } else if (family_name == "binomial") {
       # logit
-      predictions.f <- plogis(predictions.f)   
+      predictions.f <- stats::plogis(predictions.f)
     } else {
       stop("Unsupported family for intercept-only prediction: Family must be either Gaussian, Poisson, or Binomial.", call. = FALSE)
     }
@@ -121,18 +121,17 @@ predictDS <- function(newdataname, traindataname, type = c("response", "link"),
   na.action.fun <- match.fun(na.action)
 
   # Use a dummy glm object with the correct formula and family
-  dummy_fit <- glm(model_formula,
-                   data = traindata,
-                   family = family_obj,
-                   control = glm.control(maxit = 1))
+  dummy_fit <- stats::glm(model_formula,
+                          data = traindata,
+                          family = family_obj,
+                           control = stats::glm.control(maxit = 1))
 
   # Change its coefficients with the correct ones
   names(coefficients) <- names(dummy_fit$coefficients)
   dummy_fit$coefficients <- coefficients
 
   # New predictions
-  prediction <- predict(dummy_fit, newdata = newdf, type = type, na.action = na.action.fun)
+  prediction <- stats::predict(dummy_fit, newdata = newdf, type = type, na.action = na.action.fun)
 
   return(prediction)
 }
-