Training different ML models using Caret - r

Question

I am trying to train a couple of ML models using the trainControl and train functions in Caret but I am always getting the same error which just says:
Error: Stopping
without giving any more details.
The problem is the same for gbm and ranger, so I am wondering if it has something to do with a conflict of the packages I am also using in my code.
library(ggplot2)
library(lattice)
library(caret)
library(rlang)
library(tidyverse)
library(Matrix)
library(glmnet)
library(iterators)
library(parallel)
library(doParallel) # parallel processing.
registerDoParallel(cores=16)
library(randomForest)
library(gbm)
library(ranger)
library(data.table)
library(smooth)
data<- data.frame(A=seq(as.Date("2019-01-01"), by=1, len=100),B=as.numeric(runif(100, 50, 150)),C=as.numeric(runif(100, 50, 150)))
# define data sets
data_training<-data[1:60,]
data_test<-data[(60+1):nrow(data),]
# creating sampling seeds
set.seed(123)
n=nrow(data_training)
tuneLength.num <- 5
seeds <- vector(mode = "list", length = n) # creates an empty vector containing lists
for(i in 1:(n-1)){ # choose tuneLength.num random samples from 1 to 1000
seeds[[i]] <- sample.int(1000, tuneLength.num)
}
# For the last model:
seeds[[n]] <- sample.int(1000, 10)
# Define TimeControl for training and fitting:
trainingTimeControl <- trainControl(method = "timeslice",
initialWindow = 25,
horizon = 1,
fixedWindow = TRUE,
returnResamp="all",
allowParallel = TRUE,
seeds = seeds,
savePredictions = TRUE)
gbm.mod<- caret::train(B ~.- A,
data = data_training,
method = "gbm",
distribution = "gaussian",
trControl = trainingTimeControl,
tuneLength=tuneLength.num,
metric="RMSE")
EDIT: The following code works just fine:
gbm<-gbm(formula = B ~ . - A,
distribution = "gaussian",
data = data_training,
keep.data = TRUE)
It would be great if anyone has an idea what is going on here. The code is working fine with svmRadial.