diff --git a/Project.toml b/Project.toml
index 5ede357c..fb5b93a1 100644
--- a/Project.toml
+++ b/Project.toml
@@ -3,7 +3,7 @@ uuid = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 keywords = ["markov chain monte carlo", "probablistic programming"]
 license = "MIT"
 desc = "Chain types and utility functions for MCMC simulations."
-version = "6.0.4"
+version = "6.0.5"
 
 [deps]
 AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
@@ -30,16 +30,20 @@ Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 [compat]
 AbstractMCMC = "0.4, 0.5, 1.0, 2.0, 3.0, 4, 5"
 AxisArrays = "0.4.4"
+Dates = "<0.0.1, 1"
 Distributions = "0.21, 0.22, 0.23, 0.24, 0.25"
 Formatting = "0.4"
 IteratorInterfaceExtensions = "0.1.1, 1"
 KernelDensity = "0.6.2"
+LinearAlgebra = "<0.0.1, 1"
 MCMCDiagnosticTools = "0.3"
 MLJModelInterface = "0.3.5, 0.4, 1.0"
 NaturalSort = "1"
 OrderedCollections = "1.4"
 PrettyTables = "0.9, 0.10, 0.11, 0.12, 1, 2"
+Random = "<0.0.1, 1"
 RecipesBase = "0.7, 0.8, 1.0"
+Statistics = "<0.0.1, 1"
 StatsBase = "0.33.2, 0.34"
 StatsFuns = "0.8, 0.9, 1"
 TableTraits = "0.4, 1"
diff --git a/docs/Project.toml b/docs/Project.toml
index 9defdea2..e021ab6c 100644
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -10,7 +10,7 @@ MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661"
 StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
 
 [compat]
-CairoMakie = "0.6, 0.7, 0.8, 0.9, 0.10"
+CairoMakie = "0.6, 0.7, 0.8, 0.9, 0.10, 0.11"
 CategoricalArrays = "0.8, 0.9, 0.10"
 DataFrames = "0.22, 1"
 Documenter = "0.26, 0.27, 1"
diff --git a/src/plot.jl b/src/plot.jl
index 1575846c..9ef7f561 100644
--- a/src/plot.jl
+++ b/src/plot.jl
@@ -64,7 +64,7 @@ const supportedplots = push!(collect(keys(translationdict)), :mixeddensity, :cor
         lags = 0:(maxlag === nothing ? round(Int, 10 * log10(length(range(c)))) : maxlag)
         # Chains are already appended in `c` if desired, hence we use `append_chains=false`
         ac = autocor(c; sections = nothing, lags = lags, append_chains=false)
-        ac_mat = convert(Array, ac)
+        ac_mat = convert(Array{Float64}, ac)
         val = colordim == :parameter ? ac_mat[:, :, i]' : ac_mat[i, :, :]
         _AutocorPlot(lags, val)
     elseif st ∈ supportedplots
diff --git a/src/summarize.jl b/src/summarize.jl
index 70bba5d8..53af6364 100644
--- a/src/summarize.jl
+++ b/src/summarize.jl
@@ -104,8 +104,11 @@ function Base.lastindex(c::ChainDataFrame, i::Integer)
     end
 end
 
-function Base.convert(::Type{Array}, c::C) where C<:ChainDataFrame
+function Base.convert(::Type{Array}, c::ChainDataFrame)
     T = promote_eltype_namedtuple_tail(c.nt)
+    return convert(Array{T}, c)
+end
+function Base.convert(::Type{Array{T}}, c::ChainDataFrame) where {T}
     arr = Array{T, 2}(undef, c.nrows, c.ncols - 1)
 
     for (i, k) in enumerate(Iterators.drop(keys(c.nt), 1))
@@ -115,9 +118,13 @@ function Base.convert(::Type{Array}, c::C) where C<:ChainDataFrame
     return arr
 end
 
-function Base.convert(::Type{Array}, cs::Array{ChainDataFrame{T},1}) where T<:NamedTuple
+function Base.convert(::Type{Array}, cs::Vector{ChainDataFrame{NamedTuple{K,V}}}) where {K,V}
+    T = promote_eltype_tuple_type(Base.tuple_type_tail(V))
+    return convert(Array{T}, cs)
+end
+function Base.convert(::Type{Array{T}}, cs::Vector{<:ChainDataFrame}) where {T}
     return mapreduce((x, y) -> cat(x, y; dims = Val(3)), cs) do c
-        reshape(convert(Array, c), Val(3))
+        reshape(convert(Array{T}, c), Val(3))
     end
 end
 
diff --git a/test/Project.toml b/test/Project.toml
index 2883bba3..331b2b4a 100644
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -11,7 +11,6 @@ Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661"
-Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
@@ -25,17 +24,22 @@ UnicodePlots = "b8865327-cd53-5732-bb35-84acbb429228"
 [compat]
 AbstractMCMC = "2.2.1, 3.0, 4, 5"
 DataFrames = "0.22.4, 1.0"
+Dates = "<0.0.1, 1"
 Distributions = "0.24.12, 0.25"
 Documenter = "0.26, 0.27, 1"
 FFTW = "1.1"
 IteratorInterfaceExtensions = "1"
 KernelDensity = "0.6.2"
+Logging = "<0.0.1, 1"
 MCMCChains = "6"
 MLJBase = "0.18, 0.19, 0.20, 0.21, 1"
 MLJDecisionTreeInterface = "0.3, 0.4"
+Random = "<0.0.1, 1"
+Serialization = "<0.0.1, 1"
 StatsBase = "0.33.2, 0.34"
 StatsPlots = "0.14.17, 0.15"
 TableTraits = "1"
 Tables = "1.3.1"
+Test = "<0.0.1, 1"
 UnicodePlots = "2, 3"
 julia = "1.6"