diff --git a/Project.toml b/Project.toml
index f45e296..2e6e067 100644
--- a/Project.toml
+++ b/Project.toml
@@ -11,12 +11,12 @@ GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
 HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
 
 [compat]
-julia = "1.9.0"
-DataFrames = "1.5.0"
-GLM = "1.8.3"
-HypothesisTests = "0.11.0"
-Distributions = "0.25.97"
-CairoMakie = "0.10.5"
+julia = "^1.9.0"
+DataFrames = "^1.5.0"
+GLM = "^1.8.3"
+HypothesisTests = "^0.11.0"
+Distributions = "^0.25.97"
+CairoMakie = "^0.10.5"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
diff --git a/test/runtests.jl b/test/runtests.jl
index d7a169b..04b58bc 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -1,3 +1,4 @@
+using DataFrames
 using MetaAnalysis
 using Test
 
@@ -44,21 +45,30 @@ df2 = select(df, :study, :y, :v)
 
 
 # test fixed effects model 
-@test MetaAnalysis.fixedEffects(df.y, df.v)
+@testset begin
+	MetaAnalysis.fixedEffects(df.y, df.v)
+	@test true
+end
 
 # Now write the random effects model
 
 
 
 # test tausq works
-@test MetaAnalysis.tauSquare(df.y, df.v)
+@testset begin
+	MetaAnalysis.tausq(df.y, df.v)
+	@test true
+end
 
 # Now write the random effects model function
 
 
 
 # test for random effects
-@test MetaAnalysis.randEffects(df.y, df.v)
+@testset begin
+	MetaAnalysis.randEffects(df.y, df.v)
+	@test true
+end
 
 # fixed effects weights
 df.wf = 1 ./ df.v
@@ -81,5 +91,8 @@ select(df, :study, :y, :wf, :relwf, :relwr)
 
 
 
-@test MetaAnalysis.isq(df.study, df.y, df.v)
+@testset begin
+	MetaAnalysis.isq(df.study, df.y, df.v)
+	@test true
+end
 end