diff --git a/Project.toml b/Project.toml
index 3973476..87cab1c 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "AnovaGLM"
 uuid = "0a47a8e3-ec57-451e-bddb-e0be9d22772b"
 authors = ["Yu-Fong Peng <sciphypar@gmail.com>"]
-version = "0.2.3"
+version = "0.2.4"
 
 [deps]
 AnovaBase = "946dddda-6a23-4b48-8e70-8e60d9b8d680"
@@ -15,10 +15,10 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
 
 [compat]
-AnovaBase = "0.7"
+AnovaBase = "0.8"
 Distributions = "0.23, 0.24, 0.25"
 GLM = "1.9"
 Reexport = "0.2, 1"
 StatsBase = "0.33, 0.34"
 StatsModels = "0.7"
-julia = "1.6, 1.7, 1.8, 1.9"
+julia = "1.6, 1.7, 1.8, 1.9, 1.10"
diff --git a/src/anova.jl b/src/anova.jl
index 50da278..0a0c284 100644
--- a/src/anova.jl
+++ b/src/anova.jl
@@ -85,10 +85,10 @@ function anova(::Type{FTest}, aovm::FullModel{<: TRM_LM})
         end
     elseif aovm.type == 2
         fstat = ntuple(last(fullasgn) - offset) do fix
-            select1 = sort!(collect(select_super_interaction(fullpred, fix + offset)))
-            select2 = setdiff(select1, fix + offset)
-            select1 = findall(in(select1), fullasgn)
-            select2 = findall(in(select2), fullasgn)
+            s1 = sort!(collect(select_super_interaction(fullpred, fix + offset)))
+            s2 = setdiff(s1, fix + offset)
+            select1 = findall(in(s1), fullasgn)
+            select2 = findall(in(s2), fullasgn)
             (β[select1]' * (varβ[select1, select1] \ β[select1]) - β[select2]' * (varβ[select2, select2] \ β[select2])) / df[fix]
         end
     else
@@ -102,7 +102,7 @@ function anova(::Type{FTest}, aovm::FullModel{<: TRM_LM})
     devs = @. fstat * σ² * df
     dfr = round(Int, dof_residual(aovm.model))
     pvalue = @. ccdf(FDist(df, dfr), abs(fstat))
-    AnovaResult{FTest}(aovm, df, devs, fstat, pvalue, NamedTuple())
+    AnovaResult(aovm, FTest, df, devs, fstat, pvalue, NamedTuple())
 end
 
 function anova(::Type{FTest}, 
@@ -115,7 +115,7 @@ function anova(::Type{FTest},
     fstat = msr ./ dispersion(aovm.model.model, true)
     dfr = round(Int, dof_residual(aovm.model))
     pvalue = @. ccdf(FDist(df, dfr), abs(fstat))
-    AnovaResult{FTest}(aovm, df, devs, fstat, pvalue, NamedTuple())
+    AnovaResult(aovm, FTest, df, devs, fstat, pvalue, NamedTuple())
 end
 
 # ----------------------------------------------------------------------------------------
@@ -143,7 +143,7 @@ function anova(::Type{LRT},
         i -= 1
     end
     pval = @. ccdf(Chisq(df), abs(lrstat))
-    AnovaResult{LRT}(aovm, df, tuple(dev[2:end]...), lrstat, pval, NamedTuple())
+    AnovaResult(aovm, LRT, df, tuple(dev[2:end]...), lrstat, pval, NamedTuple())
 end
 
 # =================================================================================================================
@@ -160,7 +160,7 @@ function anova(::Type{FTest},
     # May exist some floating point error from dof_residual
     # check comparable and nested
     check && @warn "Could not check whether models are nested: results may not be meaningful"
-    ftest_nested(NestedModels{M}(trms), df, dfr, deviance.(trms), dispersion(last(trms).model, true))
+    ftest_nested(NestedModels(trms), df, dfr, deviance.(trms), dispersion(last(trms).model, true))
 end
 
 function anova(::Type{LRT}, 
@@ -172,7 +172,7 @@ function anova(::Type{LRT},
     df = df[ord]
     # check comparable and nested
     check && @warn "Could not check whether models are nested: results may not be meaningful"
-    lrt_nested(NestedModels{M}(trms), df, deviance.(trms), dispersion(last(trms).model, true))
+    lrt_nested(NestedModels(trms), df, deviance.(trms), dispersion(last(trms).model, true))
 end
 
 anova(::Type{FTest}, aovm::NestedModels{M}) where {M <: TableRegressionModel{<: Union{LinearModel, GeneralizedLinearModel}}} =
diff --git a/src/fit.jl b/src/fit.jl
index 36e24de..3a23936 100644
--- a/src/fit.jl
+++ b/src/fit.jl
@@ -244,7 +244,7 @@ function nestedmodels(trm::M; null::Bool = true, kwargs...) where {M <: TableReg
         y = response(mf)
         TableRegressionModel(fit(trm.mf.model, mm.m, y; wts, dropcollinear, kwargs...), mf, mm)
     end
-    NestedModels{M}(trms..., trm)
+    NestedModels(trms..., trm)
 end
 
 function nestedmodels(trm::M; null::Bool = true, kwargs...) where {M <: TableRegressionModel{<: GeneralizedLinearModel}}
@@ -262,7 +262,7 @@ function nestedmodels(trm::M; null::Bool = true, kwargs...) where {M <: TableReg
         y = response(mf)
         TableRegressionModel(fit(trm.mf.model, mm.m, y, distr, link; wts, offset, kwargs...), mf, mm)
     end
-    NestedModels{M}(trms..., trm)
+    NestedModels(trms..., trm)
 end
 
 nestedmodels(::Type{LinearModel}, formula::FormulaTerm, data; null::Bool = true, kwargs...) =