diff --git a/NEWS.md b/NEWS.md
index 54cfdae..0e93bdc 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,9 @@
+# semtree 0.9.21 (2024)
+
+- added merge functions for varimp objects
+- fixed error in to.table() which convert everything to strings
+- fixed issue in OpenMx models with unnamed parameters in matrices
+
 # semtree 0.9.20 (2024)
 
 - added an error handler for score-based tests when the vcov matrix cannot be computed (e.g., models with Heywood cases)
diff --git a/tests/testthat/test-forest-focus.R b/tests/testthat/test-forest-focus.R
index ad65c88..4e9eb15 100644
--- a/tests/testthat/test-forest-focus.R
+++ b/tests/testthat/test-forest-focus.R
@@ -2,100 +2,115 @@
 skip_on_cran()
 
 if (require("future")) {
-
-require("semtree")
-data(lgcm)
-
-
-future::plan(multisession, workers=5)
-
-lgcm$agegroup <- ordered(lgcm$agegroup)
-lgcm$training <- factor(lgcm$training)
-lgcm$noise <- as.numeric(lgcm$noise)
-
-# LOAD IN OPENMX MODEL.
-# A SIMPLE LINEAR GROWTH MODEL WITH 5 TIME POINTS FROM SIMULATED DATA
-
-manifests <- names(lgcm)[1:5]
-lgcModel <- mxModel("Linear Growth Curve Model Path Specification",
-                    type="RAM",
-                    manifestVars=manifests,
-                    latentVars=c("intercept","slope"),
-                    # residual variances
-                    mxPath(
-                      from=manifests,
-                      arrows=2,
-                      free=TRUE,
-                      values = c(1, 1, 1, 1, 1),
-                      labels=c("residual1","residual2","residual3","residual4","residual5")
-                    ),
-                    # latent variances and covariance
-                    mxPath(
-                      from=c("intercept","slope"),
-                      connect="unique.pairs",
-                      arrows=2,
-                      free=TRUE,
-                      values=c(1, 1, 1),
-                      labels=c("vari", "cov", "vars")
-                    ),
-                    # intercept loadings
-                    mxPath(
-                      from="intercept",
-                      to=manifests,
-                      arrows=1,
-                      free=FALSE,
-                      values=c(1, 1, 1, 1, 1)
-                    ),
-                    # slope loadings
-                    mxPath(
-                      from="slope",
-                      to=manifests,
-                      arrows=1,
-                      free=FALSE,
-                      values=c(0, 1, 2, 3, 4)
-                    ),
-                    # manifest means
-                    mxPath(
-                      from="one",
-                      to=manifests,
-                      arrows=1,
-                      free=FALSE,
-                      values=c(0, 0, 0, 0, 0)
-                    ),
-                    # latent means
-                    mxPath(
-                      from="one",
-                      to=c("intercept", "slope"),
-                      arrows=1,
-                      free=TRUE,
-                      values=c(1, 1),
-                      labels=c("meani", "means")
-                    ),
-                    mxData(lgcm,type="raw")
-)
-
-#tree <- semtree(model=lgcModel, data=lgcm)
-
-#lgcModel=mxRun(lgcModel)
-
-# TREE CONTROL OPTIONS.
-# TO OBTAIN BASIC/DEFAULT SMETREE OPTIONS, SIMPLY TPYE THE FOLLOWING:
-
-ctrl <- semtree.control(method = "score", verbose = TRUE)
-
-# RUN TREE.
-
-forest <- semforest(model=lgcModel, data=lgcm, control = 
-                      semforest.control(num.trees = 30, control=semtree.control(alpha=1,method="score")),
-                    constraints=semtree.constraints(focus.parameter="meani"))
-
-vim_naive <- varimp(forest)
-
-vim <- varimp(forest, method = "permutationFocus")
-
-#plot(vim)
-#plot(vim_naive)
-
-aggregate()
-
+  require("semtree")
+  data(lgcm)
+  
+  
+  future::plan(multisession, workers = 5)
+  
+  lgcm$agegroup <- ordered(lgcm$agegroup)
+  lgcm$training <- factor(lgcm$training)
+  lgcm$noise <- as.numeric(lgcm$noise)
+  
+  # LOAD IN OPENMX MODEL.
+  # A SIMPLE LINEAR GROWTH MODEL WITH 5 TIME POINTS FROM SIMULATED DATA
+  
+  manifests <- names(lgcm)[1:5]
+  lgcModel <- mxModel(
+    "Linear Growth Curve Model Path Specification",
+    type = "RAM",
+    manifestVars = manifests,
+    latentVars = c("intercept", "slope"),
+    # residual variances
+    mxPath(
+      from = manifests,
+      arrows = 2,
+      free = TRUE,
+      values = c(1, 1, 1, 1, 1),
+      labels = c(
+        "residual1",
+        "residual2",
+        "residual3",
+        "residual4",
+        "residual5"
+      )
+    ),
+    # latent variances and covariance
+    mxPath(
+      from = c("intercept", "slope"),
+      connect = "unique.pairs",
+      arrows = 2,
+      free = TRUE,
+      values = c(1, 1, 1),
+      labels = c("vari", "cov", "vars")
+    ),
+    # intercept loadings
+    mxPath(
+      from = "intercept",
+      to = manifests,
+      arrows = 1,
+      free = FALSE,
+      values = c(1, 1, 1, 1, 1)
+    ),
+    # slope loadings
+    mxPath(
+      from = "slope",
+      to = manifests,
+      arrows = 1,
+      free = FALSE,
+      values = c(0, 1, 2, 3, 4)
+    ),
+    # manifest means
+    mxPath(
+      from = "one",
+      to = manifests,
+      arrows = 1,
+      free = FALSE,
+      values = c(0, 0, 0, 0, 0)
+    ),
+    # latent means
+    mxPath(
+      from = "one",
+      to = c("intercept", "slope"),
+      arrows = 1,
+      free = TRUE,
+      values = c(1, 1),
+      labels = c("meani", "means")
+    ),
+    mxData(lgcm, type = "raw")
+  )
+  
+  #tree <- semtree(model=lgcModel, data=lgcm)
+  
+  #lgcModel=mxRun(lgcModel)
+  
+  # TREE CONTROL OPTIONS.
+  # TO OBTAIN BASIC/DEFAULT SMETREE OPTIONS, SIMPLY TPYE THE FOLLOWING:
+  
+  ctrl <- semtree.control(method = "score", verbose = TRUE)
+  
+  # RUN TREE.
+  
+  forest <- semforest(
+    model = lgcModel,
+    data = lgcm,
+    control =
+      semforest.control(
+        num.trees = 30,
+        control = semtree.control(alpha =
+                                    1, method = "score")
+      ),
+    constraints = semtree.constraints(focus.parameter =
+                                        "meani")
+  )
+  
+  vim_naive <- varimp(forest)
+  
+  vim <- varimp(forest, method = "permutationFocus")
+  
+  #plot(vim)
+  #plot(vim_naive)
+  
+ 
+  
 }
\ No newline at end of file