Multiclass Classification using expand.grid

Question

So far I built many classification models using the "caret" package. This library allows me to find the best parameters for XGBoost by using expand.grid and trying all the possible combinations of some parameters as shown in the example below.

     trControl = trainControl(
          method = 'cv',
          number = 3,
          returnData=F,
          classProbs = TRUE,
          verboseIter = TRUE,
          allowParallel = TRUE)

        tuneGridXGB <- expand.grid(
          nrounds=c(10, 50, 100, 200, 350, 500),
          max_depth = c(2,4),
          eta = c(0.005, 0.01, 0.05, 0.1),
          gamma = c(0,2,4),
          colsample_bytree = c(0.75),
          subsample = c(0.50),
          min_child_weight = c(0,2,4))

        xgbmod_classif_bin <- train(
          x=eg_Train_mat,
          y= y_train_target,
          method = "xgbTree",
          metric = "auc",
          reg_lambda=0.7,
          scale_pos_weight=1.6,
          nthread = 4,
          trControl = trControl,
          tuneGrid = tuneGridXGB,
          verbose=T)

For the first time I have a multiclass classification problem (with 9 classes) to deal with, but I don't seem to be able to use anything like "multi:softprob" (as I would do with the xgboost package - see below).

    param=list(objective="multi:softprob",   
               num_class=9,
               eta=0.005,
               max.depth=4,
               min_child_weight=2,
               gamma=6,
               eval_metric ="merror",
               nthread=4,
               booster = "gbtree",
               lambda=1.8,
               subssample=0.8,
               alpha=6,
               colsample_bytree=0.5,
               scale_pos_weight=1.6,
               verbosity=3
    )


    bst=xgboost(params = param, 
                data = eg_Train_mat, 
                nrounds = 15)

Any idea of how to try many parameters using a grid, possibly using the caret package, for a multiclass classification problem? Thanks