diff --git a/DESCRIPTION b/DESCRIPTION
index 31f70bb..86784dc 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -41,7 +41,7 @@ Imports:
     BiocParallel,
     ggcorrplot,
     magrittr,
-    Matrix
+    mlr3verse
 Suggests:
     devtools,
     testthat (>= 2.1.0),
diff --git a/R/CellTypist.R b/R/CellTypist.R
index 47b51ac..ba4687b 100644
--- a/R/CellTypist.R
+++ b/R/CellTypist.R
@@ -213,7 +213,7 @@ RunCellTypist <- function(seuratObj, modelName = "Immune_All_Low.pkl", pThreshol
 
   # Cell typist expects a single column:
   tbl <- utils::read.table(geneFile, sep = '\t')
-  write.table(tbl$V1, file = geneFile, row.names = FALSE, col.names = FALSE)
+  utils::write.table(tbl$V1, file = geneFile, row.names = FALSE, col.names = FALSE)
 
   # Ensure models present:
   if (updateModels) {
diff --git a/R/Classification.R b/R/Classification.R
index 6f4bc1c..61f6caf 100644
--- a/R/Classification.R
+++ b/R/Classification.R
@@ -101,13 +101,13 @@ TrainModel <- function(training_matrix, celltype, hyperparameter_tuning = F, lea
       learner <- mlr3::lrn("classif.ranger", importance = "permutation", predict_type = "prob")
 
       #Define Ranger Hyperparameter Space (RandomBotv2)
-      tune_ps <- ps(
-        num.trees = p_int(lower = 10, upper = 2000),
-        sample.fraction = p_dbl(lower = 0.1, upper = 1),
-        respect.unordered.factors = p_fct(levels = c("ignore", "order", "partition")),
-        min.node.size = p_int(lower = 1, upper = 100), 
-        splitrule = p_fct(levels = c("gini", "extratrees")),
-        num.random.splits = p_int(lower = 1, upper = 100, depends = splitrule == "extratrees")
+      tune_ps <- mlr3verse::ps(
+        num.trees = mlr3verse::p_int(lower = 10, upper = 2000),
+        sample.fraction = mlr3verse::p_dbl(lower = 0.1, upper = 1),
+        respect.unordered.factors = mlr3verse::p_fct(levels = c("ignore", "order", "partition")),
+        min.node.size = mlr3verse::p_int(lower = 1, upper = 100),
+        splitrule = mlr3verse::p_fct(levels = c("gini", "extratrees")),
+        num.random.splits = mlr3verse::p_int(lower = 1, upper = 100, depends = splitrule == "extratrees")
         )
     } else if (learner == "classif.xgboost"){
       #Update task
@@ -115,20 +115,20 @@ TrainModel <- function(training_matrix, celltype, hyperparameter_tuning = F, lea
       #Define learner
       learner <- mlr3::lrn("classif.xgboost", predict_type = "prob")
       #Define XGBoost model's Hyperparameter Space (RandomBotv2)
-      tune_ps <- ps(
-        booster = p_fct(levels = c("gblinear", "gbtree", "dart")),
-        nrounds = p_int(lower = 2, upper = 8, trafo = function(x) as.integer(round(exp(x)))),
-        eta = p_dbl(lower = -4, upper = 0, trafo = function(x) 10^x), 
-        gamma = p_dbl(lower = -5, upper = 1, trafo = function(x) 10^x), 
-        lambda = p_dbl(lower = -4, upper = 3, trafo = function(x) 10^x),
-        alpha = p_dbl(lower = -4, upper = 3, trafo = function(x) 10^x), 
-        subsample = p_dbl(lower = 0.1, upper = 1),
-        max_depth = p_int(lower = 1, upper = 15),
-        min_child_weight = p_dbl(lower = -1, upper = 0, trafo = function(x) 10^x),
-        colsample_bytree = p_dbl(lower = 0.1, upper = 1),
-        colsample_bylevel = p_dbl(lower = 0.1, upper = 1),
-        rate_drop = p_int(lower = 0, upper = 1, depends = booster == 'dart'),
-        skip_drop = p_int(lower = 0, upper = 1, depends = booster == 'dart')
+      tune_ps <- mlr3verse::ps(
+        booster = mlr3verse::p_fct(levels = c("gblinear", "gbtree", "dart")),
+        nrounds = mlr3verse::p_int(lower = 2, upper = 8, trafo = function(x) as.integer(round(exp(x)))),
+        eta = mlr3verse::p_dbl(lower = -4, upper = 0, trafo = function(x) 10^x),
+        gamma = mlr3verse::p_dbl(lower = -5, upper = 1, trafo = function(x) 10^x),
+        lambda = mlr3verse::p_dbl(lower = -4, upper = 3, trafo = function(x) 10^x),
+        alpha = mlr3verse::p_dbl(lower = -4, upper = 3, trafo = function(x) 10^x),
+        subsample = mlr3verse::p_dbl(lower = 0.1, upper = 1),
+        max_depth = mlr3verse::p_int(lower = 1, upper = 15),
+        min_child_weight = mlr3verse::p_dbl(lower = -1, upper = 0, trafo = function(x) 10^x),
+        colsample_bytree = mlr3verse::p_dbl(lower = 0.1, upper = 1),
+        colsample_bylevel = mlr3verse::p_dbl(lower = 0.1, upper = 1),
+        rate_drop = mlr3verse::p_int(lower = 0, upper = 1, depends = booster == 'dart'),
+        skip_drop = mlr3verse::p_int(lower = 0, upper = 1, depends = booster == 'dart')
       )
     }
   }