diff --git a/Project.toml b/Project.toml
index 07d14abd..9b4c73c4 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "LaplaceRedux"
 uuid = "c52c1a26-f7c5-402b-80be-ba1e638ad478"
 authors = ["Patrick Altmeyer"]
-version = "0.1.2"
+version = "0.1.3"
 
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
@@ -16,10 +16,8 @@ MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845"
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
-Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 Tullio = "bc48ee85-29a4-5162-ae0b-a64e1601d4bc"
@@ -37,7 +35,6 @@ MLJFlux = "0.2.10, 0.3"
 MLJModelInterface = "1.8.0"
 MLUtils = "0.4.3"
 Parameters = "0.12"
-Plots = "1"
 ProgressMeter = "1.7.2"
 Tables = "1.10.1"
 Tullio = "0.3.5"
diff --git a/src/LaplaceRedux.jl b/src/LaplaceRedux.jl
index 8cf17b93..0f5e76bf 100644
--- a/src/LaplaceRedux.jl
+++ b/src/LaplaceRedux.jl
@@ -22,6 +22,4 @@ export optimize_prior!,
 include("mlj_flux.jl")
 export LaplaceApproximation
 
-include("plotting.jl")
-
 end
diff --git a/src/plotting.jl b/src/plotting.jl
deleted file mode 100644
index 13b216f8..00000000
--- a/src/plotting.jl
+++ /dev/null
@@ -1,124 +0,0 @@
-using Plots
-
-function Plots.plot(
-    la::Laplace,
-    X::AbstractArray,
-    y::AbstractArray;
-    link_approx::Symbol=:probit,
-    target::Union{Nothing,Real}=nothing,
-    colorbar=true,
-    title=nothing,
-    length_out=50,
-    zoom=-1,
-    xlims=nothing,
-    ylims=nothing,
-    linewidth=0.1,
-    lw=4,
-    kwargs...,
-)
-    if la.likelihood == :regression
-        @assert size(X, 1) == 1 "Cannot plot regression for multiple input variables."
-    else
-        @assert size(X, 1) == 2 "Cannot plot classification for more than two input variables."
-    end
-
-    if la.likelihood == :regression
-
-        # REGRESSION
-
-        # Surface range:
-        if isnothing(xlims)
-            xlims = (minimum(X), maximum(X)) .+ (zoom, -zoom)
-        else
-            xlims = xlims .+ (zoom, -zoom)
-        end
-        if isnothing(ylims)
-            ylims = (minimum(y), maximum(y)) .+ (zoom, -zoom)
-        else
-            ylims = ylims .+ (zoom, -zoom)
-        end
-        x_range = range(xlims[1]; stop=xlims[2], length=length_out)
-        y_range = range(ylims[1]; stop=ylims[2], length=length_out)
-
-        title = isnothing(title) ? "" : title
-
-        # Plot:
-        scatter(
-            vec(X),
-            vec(y);
-            label="ytrain",
-            xlim=xlims,
-            ylim=ylims,
-            lw=lw,
-            title=title,
-            kwargs...,
-        )
-        _x = collect(x_range)[:, :]'
-        fμ, fvar = la(_x)
-        fμ = vec(fμ)
-        fσ = vec(sqrt.(fvar))
-        pred_std = sqrt.(fσ .^ 2 .+ la.σ^2)
-        plot!(
-            x_range,
-            fμ;
-            color=2,
-            label="yhat",
-            ribbon=(1.96 * pred_std, 1.96 * pred_std),
-            lw=lw,
-            kwargs...,
-        )   # the specific values 1.96 are used here to create a 95% confidence interval
-    else
-
-        # CLASSIFICATION
-
-        # Surface range:
-        if isnothing(xlims)
-            xlims = (minimum(X[1, :]), maximum(X[1, :])) .+ (zoom, -zoom)
-        else
-            xlims = xlims .+ (zoom, -zoom)
-        end
-        if isnothing(ylims)
-            ylims = (minimum(X[2, :]), maximum(X[2, :])) .+ (zoom, -zoom)
-        else
-            ylims = ylims .+ (zoom, -zoom)
-        end
-        x_range = range(xlims[1]; stop=xlims[2], length=length_out)
-        y_range = range(ylims[1]; stop=ylims[2], length=length_out)
-
-        # Plot
-        predict_ = function (X::AbstractVector)
-            z = la(X; link_approx=link_approx)
-            if outdim(la) == 1 # binary
-                z = [1.0 - z[1], z[1]]
-            end
-            return z
-        end
-        Z = [predict_([x, y]) for x in x_range, y in y_range]
-        Z = reduce(hcat, Z)
-        if outdim(la) > 1
-            if isnothing(target)
-                @info "No target label supplied, using first."
-            end
-            target = isnothing(target) ? 1 : target
-            title = isnothing(title) ? "p̂(y=$(target))" : title
-        else
-            target = isnothing(target) ? 2 : target
-            title = isnothing(title) ? "p̂(y=$(target-1))" : title
-        end
-
-        # Contour:
-        contourf(
-            x_range,
-            y_range,
-            Z[Int(target), :];
-            colorbar=colorbar,
-            title=title,
-            linewidth=linewidth,
-            xlims=xlims,
-            ylims=ylims,
-            kwargs...,
-        )
-        # Samples:
-        scatter!(X[1, :], X[2, :]; group=Int.(y), color=Int.(y), kwargs...)
-    end
-end