diff --git a/docs/src/tutorials/spectral_DCM.jl b/docs/src/tutorials/spectral_DCM.jl
new file mode 100644
index 0000000..fe73c7c
--- /dev/null
+++ b/docs/src/tutorials/spectral_DCM.jl
@@ -0,0 +1,238 @@
+using Neuroblox
+
+using LinearAlgebra
+using Graphs
+using DifferentialEquations
+using DataFrames
+using OrderedCollections
+using CairoMakie
+using ModelingToolkit
+
+########## Assemble model ##########
+# In this tutorial we will define a circuit of three linear neuronal mass models, all driven by an Ornstein-Uhlenbeck process.
+# We will model fMRI data by a balloon model and BOLD signal on top.
+# After simulation of this simple model we will use spectral Dynamic Causal Modeling to infer some of the model parameters from the simulation time series. 
+# Step 1: define the graph, add blocks
+# Step 2: simulate the model
+# Step 3: compute the cross spectral density
+# Step 4: setup the DCM
+# Step 5: estimate
+# Step 5: plot the results
+
+# ## Define the model
+# We will define a model of 3 regions. This means first of all to define a graph.
+# To this graph we will add three linear neuronal mass models which constitute the (hidden) neuronal dynamics.
+# These constitute three nodes of the graph.
+# Next we will also need some input that stimulates the activity, we use simple Ornstein-Uhlenbeck blocks to create stochastic inputs.
+# One per region.
+# We want to simulate fMRI signals thus we will need to also add a BalloonModel per region.
+# Note that the Ornstein-Uhlenbeck block will feed into the linear neural mass which in turn will feed into the BalloonModel blox.
+# This needs to be represented by the way we define the edges.
+nr = 3             # number of regions
+g = MetaDiGraph()
+regions = []   # list of neural mass blocks to then connect them to each other with an adjacency matrix
+
+# Now add the different blocks to each region and connect the blocks within each region:
+for i = 1:nr
+    region = LinearNeuralMass(;name=Symbol("r$(i)₊lm"))
+    push!(regions, region)          # store neural mass model for connection of regions
+
+    ## add Ornstein-Uhlenbeck block as noisy input to the current region
+    input = OUBlox(;name=Symbol("r$(i)₊ou"), σ=0.1)
+    add_edge!(g, input => region; :weight => 1/16)   # Note that 1/16 is taken from SPM12, this stabilizes the balloon model simulation. Alternatively the noise of the Ornstein-Uhlenbeck block or the weight of the edge connecting neuronal activity and balloon model could be reduced to guarantee numerical stability.
+
+    ## simulate fMRI signal with BalloonModel which includes the BOLD signal on top of the balloon model dynamics
+    measurement = BalloonModel(;name=Symbol("r$(i)₊bm"))
+    add_edge!(g, region => measurement; :weight => 1.0)
+end
+# Next we define the between-region connectivity matrix and make sure that it is diagonally dominant to guarantee numerical stability (see Gershgorin theorem).
+A_true = randn(nr, nr)
+A_true -= diagm(map(a -> sum(abs, a), eachrow(A_true)))    # ensure diagonal dominance of matrix
+for idx in CartesianIndices(A_true)
+    add_edge!(g, regions[idx[1]] => regions[idx[2]]; :weight => A_true[idx[1], idx[2]])
+end
+
+# finally we compose the simulation model
+@named simmodel = system_from_graph(g)
+simmodel = structural_simplify(simmodel, split=false)
+
+# setup simulation of the model, time in seconds
+tspan = (0.0, 512.0)
+prob = SDEProblem(simmodel, [], tspan)
+dt = 2.0   # two seconds as measurement interval for fMRI
+sol = solve(prob, saveat=dt);
+
+# ##Plot simulation results
+
+# plot bold signal time series
+idx_m = get_idx_tagged_vars(simmodel, "measurement")    # get index of bold signal
+f = Figure()
+ax = Axis(f[1, 1],
+    title = "fMRI time series",
+    xlabel = "Time [s]",
+    ylabel = "BOLD",
+)
+lines!(ax, sol, idxs=idx_m)
+f
+## (Note to self: tosymbol(x; escape=false)
+
+# ##Estimate and plot cross-spectrum
+dfsol = DataFrame(sol)
+data = Matrix(dfsol[:, idx_m])
+# We compute the cross-spectral density by fitting a linear model of order `p` and then compute the csd analytically from the parameters of the multivariate autoregressive model
+p = 8
+mar = mar_ml(data, p)   # maximum likelihood estimation of the MAR coefficients and noise covariance matrix
+ns = size(data, 1)
+freq = range(min(128, ns*dt)^-1, max(8, 2*dt)^-1, 32)
+csd = mar2csd(mar, freq, dt^-1)
+# Now plot the cross-spectrum:
+fig = Figure(size=(1200, 800))
+grid = fig[1, 1] = GridLayout()
+for i = 1:nr
+    for j = 1:nr
+        ax = Axis(grid[i, j])
+        lines!(ax, freq, real.(csd[:, i, j]))
+        if (i==1) && (j==2)
+            ax.title = "Cross spectral density"
+        end
+        if (i==3) && (j==2)
+            ax.xlabel = "Frequency [Hz]"
+        end
+    end
+end
+fig
+
+
+# #Model inference
+
+# We will now assemble a new model that is used for fitting the previous simulations.
+# This procedure is similar to before with the difference that we will define global parameters and use tags such as [tunable=false/true] to define which parameters we will want to estimate.
+# Note that parameters are tunable by default.
+g = MetaDiGraph()
+regions = []   # list of neural mass blocks to then connect them to each other with an adjacency matrix
+
+# The following parameters are shared accross regions, which is why we define them here. 
+@parameters lnκ=0.0 [tunable=false] lnϵ=0.0 [tunable=false] lnτ=0.0 [tunable=false]   # lnκ: decay parameter for hemodynamics; lnϵ: ratio of intra- to extra-vascular components, lnτ: transit time scale
+@parameters C=1/16 [tunable=false]   # note that C=1/16 is taken from SPM12 and stabilizes the balloon model simulation. See also comment above.
+
+for i = 1:nr
+    region = LinearNeuralMass(;name=Symbol("r$(i)₊lm"))
+    push!(regions, region)
+    input = ExternalInput(;name=Symbol("r$(i)₊ei"))
+    add_edge!(g, input => region; :weight => C)
+
+    ## we assume fMRI signal and model them with a BalloonModel
+    measurement = BalloonModel(;name=Symbol("r$(i)₊bm"), lnτ=lnτ, lnκ=lnκ, lnϵ=lnϵ)
+    add_edge!(g, region => measurement; :weight => 1.0)
+end
+
+A_prior = 0.01*randn(nr, nr)
+A_prior -= diagm(diag(A_prior))    # ensure diagonal dominance of matrix
+# Since we want to optimize these weights we turn them into symbolic parameters:
+# Add the symbolic weights to the edges and connect reegions.
+@parameters A[1:nr^2] = vec(A_prior) [tunable = true]
+for (i, idx) in enumerate(CartesianIndices(A_prior))
+    if idx[1] == idx[2]
+        add_edge!(g, regions[idx[1]] => regions[idx[2]]; :weight => -exp(A[i])/2)  # -exp(A[i])/2: treatement of diagonal elements in SPM12 to make diagonal dominance (see Gershgorin Theorem) more likely but it is not guaranteed
+    else
+        add_edge!(g, regions[idx[2]] => regions[idx[1]]; :weight => A[i])
+    end
+end
+
+@named fitmodel = system_from_graph(g)
+fitmodel = structural_simplify(fitmodel, split=false)
+
+
+# #Setup DCM
+max_iter = 128            # maximum number of iterations
+
+# TODO: Place all this part into a helper function that defines the priors until `csdsetup`.
+# attribute initial conditions to states
+sts, idx_sts = get_dynamic_states(fitmodel)
+idx_u = get_idx_tagged_vars(fitmodel, "ext_input")         # get index of external input state
+idx_bold, s_bold = get_eqidx_tagged_vars(fitmodel, "measurement")  # get index of equation of bold state
+
+# the following step is needed if the model's Jacobian would give degenerate eigenvalues if expanded around 0 (which is the default expansion)
+perturbedfp = Dict(sts .=> abs.(0.001*rand(length(sts))))     # slight noise to avoid issues with Automatic Differentiation. TODO: find different solution, this is hacky.
+
+modelparam = OrderedDict()
+for par in tunable_parameters(fitmodel)
+    modelparam[par] = Symbolics.getdefaultval(par)
+end
+np = length(modelparam)
+indices = Dict(:dspars => collect(1:np))
+# Noise parameter mean
+modelparam[:lnα] = [0.0, 0.0];           # intrinsic fluctuations, ln(α) as in equation 2 of Friston et al. 2014 
+n = length(modelparam[:lnα]);
+indices[:lnα] = collect(np+1:np+n);
+np += n;
+modelparam[:lnβ] = [0.0, 0.0];           # global observation noise, ln(β) as above
+n = length(modelparam[:lnβ]);
+indices[:lnβ] = collect(np+1:np+n);
+np += n;
+modelparam[:lnγ] = zeros(Float64, nr);   # region specific observation noise
+indices[:lnγ] = collect(np+1:np+nr);
+np += nr
+indices[:u] = idx_u
+indices[:m] = idx_bold
+indices[:sts] = idx_sts
+
+# define prior variances
+paramvariance = copy(modelparam)
+paramvariance[:lnα] = ones(Float64, length(modelparam[:lnα]))./64.0; 
+paramvariance[:lnβ] = ones(Float64, length(modelparam[:lnβ]))./64.0;
+paramvariance[:lnγ] = ones(Float64, nr)./64.0;
+for (k, v) in paramvariance
+    if occursin("A[", string(k))
+        paramvariance[k] = ones(length(v));
+    elseif occursin("κ", string(k))
+        paramvariance[k] = ones(length(v))./256.0;
+    elseif occursin("ϵ", string(k))
+        paramvariance[k] = 1/256.0;
+    elseif occursin("τ", string(k))
+        paramvariance[k] = 1/256.0;
+    end
+end
+
+priors = DataFrame(name=[k for k in keys(modelparam)], mean=[m for m in values(modelparam)], variance=[v for v in values(paramvariance)])
+hyperpriors = Dict(:Πλ_pr => 128.0*ones(1, 1),   # prior metaparameter precision, needs to be a matrix
+                   :μλ_pr => [8.0]               # prior metaparameter mean, needs to be a vector
+                  );
+
+csdsetup = (mar_order = p, freq = freq, dt = dt);
+
+(state, setup) = setup_sDCM(dfsol[:, [s[1:end-3] for s in String.(Symbol.(s_bold))]], fitmodel, perturbedfp, csdsetup, priors, hyperpriors, indices, modelparam, "fMRI");
+
+# HACK: on machines with very small amounts of RAM, Julia can run out of stack space while compiling the code called in this loop
+# this should be rewritten to abuse the compiler less, but for now, an easy solution is just to run it with more allocated stack space.
+with_stack(f, n) = fetch(schedule(Task(f, n)))
+
+with_stack(5_000_000) do  # 5MB of stack space
+    for iter in 1:max_iter
+        state.iter = iter
+        run_sDCM_iteration!(state, setup)
+        print("iteration: ", iter, " - F:", state.F[end] - state.F[2], " - dF predicted:", state.dF[end], "\n")
+        if iter >= 4
+            criterion = state.dF[end-3:end] .< setup.tolerance
+            if all(criterion)
+                print("convergence\n")
+                break
+            end
+        end
+    end
+end
+
+# ##Plot Results
+# Later place all into one figure using [Makie-style layouts](https://docs.makie.org/stable/tutorials/layout-tutorial)
+# Plot the free energy evolution:
+freeenergy(state)
+
+# Plot the estimated posterior of the effective connectivity and compare that to the true parameter values.
+# Bar hight are the posterior mean and error bars are the standard deviation of the posterior.
+
+ecbarplot(state, setup, A_true)
+## TODO: need to implement a ecbarplot! to be able to integrate into one figure
+
+
+using Literate
+Literate.markdown("./docs/src/tutorials/spectral_DCM.jl", "./docs/src/tutorials/")
\ No newline at end of file
diff --git a/docs/src/tutorials/spectral_DCM.md b/docs/src/tutorials/spectral_DCM.md
new file mode 100644
index 0000000..2fafa95
--- /dev/null
+++ b/docs/src/tutorials/spectral_DCM.md
@@ -0,0 +1,314 @@
+```@meta
+EditURL = "spectral_DCM.jl"
+```
+
+````@example spectral_DCM
+using Neuroblox
+
+using LinearAlgebra
+using Graphs
+using DifferentialEquations
+using DataFrames
+using OrderedCollections
+using CairoMakie
+using ModelingToolkit
+
+########## Assemble model ##########
+````
+
+In this tutorial we will define a circuit of three linear neuronal mass models, all driven by an Ornstein-Uhlenbeck process.
+We will model fMRI data by a balloon model and BOLD signal on top.
+After simulation of this simple model we will use spectral Dynamic Causal Modeling to infer some of the model parameters from the simulation time series.
+Step 1: define the graph, add blocks
+Step 2: simulate the model
+Step 3: compute the cross spectral density
+Step 4: setup the DCM
+Step 5: estimate
+Step 5: plot the results
+
+## Define the model
+We will define a model of 3 regions. This means first of all to define a graph.
+To this graph we will add three linear neuronal mass models which constitute the (hidden) neuronal dynamics.
+These constitute three nodes of the graph.
+Next we will also need some input that stimulates the activity, we use simple Ornstein-Uhlenbeck blocks to create stochastic inputs.
+One per region.
+We want to simulate fMRI signals thus we will need to also add a BalloonModel per region.
+Note that the Ornstein-Uhlenbeck block will feed into the linear neural mass which in turn will feed into the BalloonModel blox.
+This needs to be represented by the way we define the edges.
+
+````@example spectral_DCM
+nr = 3             # number of regions
+g = MetaDiGraph()
+regions = []   # list of neural mass blocks to then connect them to each other with an adjacency matrix
+````
+
+Now add the different blocks to each region and connect the blocks within each region:
+
+````@example spectral_DCM
+for i = 1:nr
+    region = LinearNeuralMass(;name=Symbol("r$(i)₊lm"))
+    push!(regions, region)          # store neural mass model for connection of regions
+
+    # add Ornstein-Uhlenbeck block as noisy input to the current region
+    input = OUBlox(;name=Symbol("r$(i)₊ou"), σ=0.1)
+    add_edge!(g, input => region; :weight => 1/16)   # Note that 1/16 is taken from SPM12, this stabilizes the balloon model simulation. Alternatively the noise of the Ornstein-Uhlenbeck block or the weight of the edge connecting neuronal activity and balloon model could be reduced to guarantee numerical stability.
+
+    # simulate fMRI signal with BalloonModel which includes the BOLD signal on top of the balloon model dynamics
+    measurement = BalloonModel(;name=Symbol("r$(i)₊bm"))
+    add_edge!(g, region => measurement; :weight => 1.0)
+end
+````
+
+Next we define the between-region connectivity matrix and make sure that it is diagonally dominant to guarantee numerical stability (see Gershgorin theorem).
+
+````@example spectral_DCM
+A_true = randn(nr, nr)
+A_true -= diagm(map(a -> sum(abs, a), eachrow(A_true)))    # ensure diagonal dominance of matrix
+for idx in CartesianIndices(A_true)
+    add_edge!(g, regions[idx[1]] => regions[idx[2]]; :weight => A_true[idx[1], idx[2]])
+end
+````
+
+finally we compose the simulation model
+
+````@example spectral_DCM
+@named simmodel = system_from_graph(g)
+simmodel = structural_simplify(simmodel, split=false)
+````
+
+setup simulation of the model, time in seconds
+
+````@example spectral_DCM
+tspan = (0.0, 512.0)
+prob = SDEProblem(simmodel, [], tspan)
+dt = 2.0   # two seconds as measurement interval for fMRI
+sol = solve(prob, saveat=dt);
+nothing #hide
+````
+
+##Plot simulation results
+
+plot bold signal time series
+
+````@example spectral_DCM
+idx_m = get_idx_tagged_vars(simmodel, "measurement")    # get index of bold signal
+f = Figure()
+ax = Axis(f[1, 1],
+    title = "fMRI time series",
+    xlabel = "Time [s]",
+    ylabel = "BOLD",
+)
+lines!(ax, sol, idxs=idx_m)
+f
+# (Note to self: tosymbol(x; escape=false)
+````
+
+##Estimate and plot cross-spectrum
+
+````@example spectral_DCM
+dfsol = DataFrame(sol)
+data = Matrix(dfsol[:, idx_m])
+````
+
+We compute the cross-spectral density by fitting a linear model of order `p` and then compute the csd analytically from the parameters of the multivariate autoregressive model
+
+````@example spectral_DCM
+p = 8
+mar = mar_ml(data, p)   # maximum likelihood estimation of the MAR coefficients and noise covariance matrix
+ns = size(data, 1)
+freq = range(min(128, ns*dt)^-1, max(8, 2*dt)^-1, 32)
+csd = mar2csd(mar, freq, dt^-1)
+````
+
+Now plot the cross-spectrum:
+
+````@example spectral_DCM
+fig = Figure(size=(1200, 800))
+grid = fig[1, 1] = GridLayout()
+for i = 1:nr
+    for j = 1:nr
+        ax = Axis(grid[i, j])
+        lines!(ax, freq, real.(csd[:, i, j]))
+        if (i==1) && (j==2)
+            ax.title = "Cross spectral density"
+        end
+        if (i==3) && (j==2)
+            ax.xlabel = "Frequency [Hz]"
+        end
+    end
+end
+fig
+````
+
+#Model inference
+
+We will now assemble a new model that is used for fitting the previous simulations.
+This procedure is similar to before with the difference that we will define global parameters and use tags such as [tunable=false/true] to define which parameters we will want to estimate.
+Note that parameters are tunable by default.
+
+````@example spectral_DCM
+g = MetaDiGraph()
+regions = []   # list of neural mass blocks to then connect them to each other with an adjacency matrix
+````
+
+The following parameters are shared accross regions, which is why we define them here.
+
+````@example spectral_DCM
+@parameters lnκ=0.0 [tunable=false] lnϵ=0.0 [tunable=false] lnτ=0.0 [tunable=false]   # lnκ: decay parameter for hemodynamics; lnϵ: ratio of intra- to extra-vascular components, lnτ: transit time scale
+@parameters C=1/16 [tunable=false]   # note that C=1/16 is taken from SPM12 and stabilizes the balloon model simulation. See also comment above.
+
+for i = 1:nr
+    region = LinearNeuralMass(;name=Symbol("r$(i)₊lm"))
+    push!(regions, region)
+    input = ExternalInput(;name=Symbol("r$(i)₊ei"))
+    add_edge!(g, input => region; :weight => C)
+
+    # we assume fMRI signal and model them with a BalloonModel
+    measurement = BalloonModel(;name=Symbol("r$(i)₊bm"), lnτ=lnτ, lnκ=lnκ, lnϵ=lnϵ)
+    add_edge!(g, region => measurement; :weight => 1.0)
+end
+
+A_prior = 0.01*randn(nr, nr)
+A_prior -= diagm(diag(A_prior))    # ensure diagonal dominance of matrix
+````
+
+Since we want to optimize these weights we turn them into symbolic parameters:
+Add the symbolic weights to the edges and connect reegions.
+
+````@example spectral_DCM
+@parameters A[1:nr^2] = vec(A_prior) [tunable = true]
+for (i, idx) in enumerate(CartesianIndices(A_prior))
+    if idx[1] == idx[2]
+        add_edge!(g, regions[idx[1]] => regions[idx[2]]; :weight => -exp(A[i])/2)  # -exp(A[i])/2: treatement of diagonal elements in SPM12 to make diagonal dominance (see Gershgorin Theorem) more likely but it is not guaranteed
+    else
+        add_edge!(g, regions[idx[2]] => regions[idx[1]]; :weight => A[i])
+    end
+end
+
+@named fitmodel = system_from_graph(g)
+fitmodel = structural_simplify(fitmodel, split=false)
+````
+
+#Setup DCM
+
+````@example spectral_DCM
+max_iter = 128            # maximum number of iterations
+````
+
+TODO: Place all this part into a helper function that defines the priors until `csdsetup`.
+attribute initial conditions to states
+
+````@example spectral_DCM
+sts, idx_sts = get_dynamic_states(fitmodel)
+idx_u = get_idx_tagged_vars(fitmodel, "ext_input")         # get index of external input state
+idx_bold, s_bold = get_eqidx_tagged_vars(fitmodel, "measurement")  # get index of equation of bold state
+````
+
+the following step is needed if the model's Jacobian would give degenerate eigenvalues if expanded around 0 (which is the default expansion)
+
+````@example spectral_DCM
+perturbedfp = Dict(sts .=> abs.(0.001*rand(length(sts))))     # slight noise to avoid issues with Automatic Differentiation. TODO: find different solution, this is hacky.
+
+modelparam = OrderedDict()
+for par in tunable_parameters(fitmodel)
+    modelparam[par] = Symbolics.getdefaultval(par)
+end
+np = length(modelparam)
+indices = Dict(:dspars => collect(1:np))
+````
+
+Noise parameter mean
+
+````@example spectral_DCM
+modelparam[:lnα] = [0.0, 0.0];           # intrinsic fluctuations, ln(α) as in equation 2 of Friston et al. 2014
+n = length(modelparam[:lnα]);
+indices[:lnα] = collect(np+1:np+n);
+np += n;
+modelparam[:lnβ] = [0.0, 0.0];           # global observation noise, ln(β) as above
+n = length(modelparam[:lnβ]);
+indices[:lnβ] = collect(np+1:np+n);
+np += n;
+modelparam[:lnγ] = zeros(Float64, nr);   # region specific observation noise
+indices[:lnγ] = collect(np+1:np+nr);
+np += nr
+indices[:u] = idx_u
+indices[:m] = idx_bold
+indices[:sts] = idx_sts
+````
+
+define prior variances
+
+````@example spectral_DCM
+paramvariance = copy(modelparam)
+paramvariance[:lnα] = ones(Float64, length(modelparam[:lnα]))./64.0;
+paramvariance[:lnβ] = ones(Float64, length(modelparam[:lnβ]))./64.0;
+paramvariance[:lnγ] = ones(Float64, nr)./64.0;
+for (k, v) in paramvariance
+    if occursin("A[", string(k))
+        paramvariance[k] = ones(length(v));
+    elseif occursin("κ", string(k))
+        paramvariance[k] = ones(length(v))./256.0;
+    elseif occursin("ϵ", string(k))
+        paramvariance[k] = 1/256.0;
+    elseif occursin("τ", string(k))
+        paramvariance[k] = 1/256.0;
+    end
+end
+
+priors = DataFrame(name=[k for k in keys(modelparam)], mean=[m for m in values(modelparam)], variance=[v for v in values(paramvariance)])
+hyperpriors = Dict(:Πλ_pr => 128.0*ones(1, 1),   # prior metaparameter precision, needs to be a matrix
+                   :μλ_pr => [8.0]               # prior metaparameter mean, needs to be a vector
+                  );
+
+csdsetup = (mar_order = p, freq = freq, dt = dt);
+
+(state, setup) = setup_sDCM(dfsol[:, [s[1:end-3] for s in String.(Symbol.(s_bold))]], fitmodel, perturbedfp, csdsetup, priors, hyperpriors, indices, modelparam, "fMRI");
+nothing #hide
+````
+
+HACK: on machines with very small amounts of RAM, Julia can run out of stack space while compiling the code called in this loop
+this should be rewritten to abuse the compiler less, but for now, an easy solution is just to run it with more allocated stack space.
+
+````@example spectral_DCM
+with_stack(f, n) = fetch(schedule(Task(f, n)))
+
+with_stack(5_000_000) do  # 5MB of stack space
+    for iter in 1:max_iter
+        state.iter = iter
+        run_sDCM_iteration!(state, setup)
+        print("iteration: ", iter, " - F:", state.F[end] - state.F[2], " - dF predicted:", state.dF[end], "\n")
+        if iter >= 4
+            criterion = state.dF[end-3:end] .< setup.tolerance
+            if all(criterion)
+                print("convergence\n")
+                break
+            end
+        end
+    end
+end
+````
+
+##Plot Results
+Later place all into one figure using [Makie-style layouts](https://docs.makie.org/stable/tutorials/layout-tutorial)
+Plot the free energy evolution:
+
+````@example spectral_DCM
+freeenergy(state)
+````
+
+Plot the estimated posterior of the effective connectivity and compare that to the true parameter values.
+Bar hight are the posterior mean and error bars are the standard deviation of the posterior.
+
+````@example spectral_DCM
+ecbarplot(state, setup, A_true)
+# TODO: need to implement a ecbarplot! to be able to integrate into one figure
+
+
+using Literate
+Literate.markdown("./docs/src/tutorials/spectral_DCM.jl", "./docs/src/tutorials/")
+````
+
+---
+
+*This page was generated using [Literate.jl](https://github.com/fredrikekre/Literate.jl).*
+