diff --git a/.JuliaFormatter.toml b/.JuliaFormatter.toml
index 9c79359..959ad88 100644
--- a/.JuliaFormatter.toml
+++ b/.JuliaFormatter.toml
@@ -1,2 +1,3 @@
 style = "sciml"
-format_markdown = true
\ No newline at end of file
+format_markdown = true
+format_docstrings = true
\ No newline at end of file
diff --git a/README.md b/README.md
index aaa3b02..a47546f 100644
--- a/README.md
+++ b/README.md
@@ -197,7 +197,7 @@ names = [
     "Shift",
     "Digital Shift",
     "Matousek Scramble",
-    "Owen Scramble",
+    "Owen Scramble"
 ]
 p = [plot(thickness_scaling = 1.5, aspect_ratio = :equal) for i in sequences]
 for (i, x) in enumerate(sequences)
diff --git a/docs/make.jl b/docs/make.jl
index a32f1c8..487a252 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -12,7 +12,7 @@ makedocs(sitename = "QuasiMonteCarlo.jl",
     warnonly = [:missing_docs],
     linkcheck_ignore = [
         "https://www.sciencedirect.com/science/article/pii/S0010465509003087",
-        "https://artowen.su.domains/mc/qmcstuff.pdf",
+        "https://artowen.su.domains/mc/qmcstuff.pdf"
     ],
     format = Documenter.HTML(assets = ["assets/favicon.ico"],
         canonical = "https://docs.sciml.ai/QuasiMonteCarlo/stable/"),
diff --git a/docs/pages.jl b/docs/pages.jl
index fdd319c..bc70c63 100644
--- a/docs/pages.jl
+++ b/docs/pages.jl
@@ -5,5 +5,5 @@ pages = [
     "samplers.md",
     "randomization.md",
     "design_matrix.md",
-    "types.md",
+    "types.md"
 ]
diff --git a/docs/src/design_matrix.md b/docs/src/design_matrix.md
index 6f78481..7bdfadc 100644
--- a/docs/src/design_matrix.md
+++ b/docs/src/design_matrix.md
@@ -48,7 +48,8 @@ Using `std(μ)` then gives you the estimated variance of your RQMC prediction.
 
 ```@example 2
 # Or using `generate_design_matrices`. Note that this is less memory efficient since it allocate space for 5 large big matrices.
-μ = [mean(f(c) for c in eachcol(X)) for X in QuasiMonteCarlo.generate_design_matrices(N,
+μ = [mean(f(c) for c in eachcol(X))
+     for X in QuasiMonteCarlo.generate_design_matrices(N,
     d,
     FaureSample(R = OwenScramble(base = b, pad = pad)),
     num_mats)]
diff --git a/docs/src/randomization.md b/docs/src/randomization.md
index c3e8fe5..27b2594 100644
--- a/docs/src/randomization.md
+++ b/docs/src/randomization.md
@@ -143,7 +143,7 @@ names = [
     "Shift",
     "Digital Shift",
     "Matousek Scramble",
-    "Owen Scramble",
+    "Owen Scramble"
 ]
 p = [plot(thickness_scaling = 1.5, aspect_ratio = :equal) for i in sequences]
 for (i, x) in enumerate(sequences)
diff --git a/ext/QuasiMonteCarloDistributionsExt.jl b/ext/QuasiMonteCarloDistributionsExt.jl
index bd91374..4d01713 100644
--- a/ext/QuasiMonteCarloDistributionsExt.jl
+++ b/ext/QuasiMonteCarloDistributionsExt.jl
@@ -22,9 +22,9 @@ Return a point set from a distribution `D`:
   - `ub` is the upper bound. Its dimension must match `length(lb)`.
 """
 function QuasiMonteCarlo.sample(n::Integer,
-    d::Integer,
-    D::Distributions.Sampleable,
-    T = eltype(D))
+        d::Integer,
+        D::Distributions.Sampleable,
+        T = eltype(D))
     @assert n>0 QuasiMonteCarlo.ZERO_SAMPLES_MESSAGE
     x = [[rand(D) for j in 1:d] for i in 1:n]
     return reduce(hcat, x)
@@ -51,8 +51,8 @@ Return a QMC point set where:
 In the first method the type of the point set is specified by `T` while in the second method the output type is inferred from the bound types.
 """
 function QuasiMonteCarlo.sample(n::Integer, lb::T, ub::T,
-    S::D) where {T <: Union{Base.AbstractVecOrTuple, Number},
-    D <: Distributions.Sampleable}
+        S::D) where {T <: Union{Base.AbstractVecOrTuple, Number},
+        D <: Distributions.Sampleable}
     QuasiMonteCarlo._check_sequence(lb, ub, n)
     lb = float.(lb)
     ub = float.(ub)
@@ -61,10 +61,10 @@ function QuasiMonteCarlo.sample(n::Integer, lb::T, ub::T,
 end
 
 function QuasiMonteCarlo.DesignMatrix(N,
-    d,
-    D::Distributions.Sampleable,
-    num_mats,
-    T = Float64)
+        d,
+        D::Distributions.Sampleable,
+        num_mats,
+        T = Float64)
     X = QuasiMonteCarlo.initialize(N, d, D, T)
     return QuasiMonteCarlo.DistributionDesignMat(X, D, num_mats)
 end
diff --git a/src/Faure.jl b/src/Faure.jl
index d7498b8..88b234c 100644
--- a/src/Faure.jl
+++ b/src/Faure.jl
@@ -11,7 +11,7 @@ end
 
 # raise a Pascal matrix to a given power.
 @fastmath function pascal_power!(result::UpperTriangular, pascal::UpperTriangular,
-    power::Integer, base::Integer)
+        power::Integer, base::Integer)
     @inbounds @simd for idx in eachindex(pascal)
         i, j = Tuple(idx)
         i ≤ j ? (result[idx] = powermod(power, j - i, base) * pascal[idx]) : result[idx]
@@ -45,7 +45,7 @@ Base.@kwdef struct FaureSample <: DeterministicSamplingAlgorithm
 end
 
 @fastmath function sample(n::Integer, dimension::Integer, S::FaureSample, T = Float64;
-    skipchecks = false)
+        skipchecks = false)
     base = nextprime(dimension)
     n_digits = ceil(Int, log(base, n))
     power = n_digits - 1
@@ -60,7 +60,7 @@ end
 end
 
 @fastmath @views function _faure_samples(n_samples::I, n_digits::I, dimension::I,
-    ::Type{F} = Float64) where {I <: Integer, F}
+        ::Type{F} = Float64) where {I <: Integer, F}
     base = nextprime(dimension)
     inv_base = inv(base)
 
diff --git a/src/Kronecker.jl b/src/Kronecker.jl
index 7d5a81e..c8780e8 100644
--- a/src/Kronecker.jl
+++ b/src/Kronecker.jl
@@ -36,7 +36,7 @@ function sample(n::Integer, d::Integer, S::KroneckerSample{Missing}, T = Float64
 end
 
 function sample(n::Integer, d::Integer, k::KroneckerSample{V},
-    T) where {V <: AbstractVector}
+        T) where {V <: AbstractVector}
     @assert eltype(V)==T "Sample type must match generator type."
     return randomize(sample(n, d, k), k.R)
 end
diff --git a/src/Lattices.jl b/src/Lattices.jl
index 1a8bfb0..d737121 100644
--- a/src/Lattices.jl
+++ b/src/Lattices.jl
@@ -12,7 +12,7 @@ Base.@kwdef @concrete struct GridSample <: DeterministicSamplingAlgorithm
 end
 
 function sample(n::Integer, d::Integer, S::GridSample, T = Float64)
-    samples = rand.(range.(zeros(T, d), ones(T,d); length = n+1), Ref(n))
+    samples = rand.(range.(zeros(T, d), ones(T, d); length = n + 1), Ref(n))
     randomize(mapreduce(permutedims, vcat, samples), S.R)
 end
 
diff --git a/src/QuasiMonteCarlo.jl b/src/QuasiMonteCarlo.jl
index 74697ed..9bb734b 100644
--- a/src/QuasiMonteCarlo.jl
+++ b/src/QuasiMonteCarlo.jl
@@ -97,24 +97,24 @@ include("RandomizedQuasiMonteCarlo/scrambling_base_b.jl")
 include("RandomizedQuasiMonteCarlo/iterators.jl")
 
 export SamplingAlgorithm,
-    GridSample,
-    SobolSample,
-    LatinHypercubeSample,
-    RandomizedHaltonSample,
-    LatticeRuleSample,
-    RandomSample,
-    HaltonSample,
-    VanDerCorputSample,
-    GoldenSample,
-    KroneckerSample,
-    FaureSample,
-    randomize,
-    RandomizationMethod,
-    NoRand,
-    Shift,
-    ScrambleMethod,
-    OwenScramble,
-    MatousekScramble,
-    DigitalShift,
-    DesignMatrix
+       GridSample,
+       SobolSample,
+       LatinHypercubeSample,
+       RandomizedHaltonSample,
+       LatticeRuleSample,
+       RandomSample,
+       HaltonSample,
+       VanDerCorputSample,
+       GoldenSample,
+       KroneckerSample,
+       FaureSample,
+       randomize,
+       RandomizationMethod,
+       NoRand,
+       Shift,
+       ScrambleMethod,
+       OwenScramble,
+       MatousekScramble,
+       DigitalShift,
+       DesignMatrix
 end # module
diff --git a/src/RandomizedQuasiMonteCarlo/iterators.jl b/src/RandomizedQuasiMonteCarlo/iterators.jl
index 19e5920..f0fa8d9 100644
--- a/src/RandomizedQuasiMonteCarlo/iterators.jl
+++ b/src/RandomizedQuasiMonteCarlo/iterators.jl
@@ -8,6 +8,7 @@ end
 
 """
     OwenDesignMat{T<:Real, I<:Integer, F<:Integer} <: AbstractDesignMatrix
+
 Create an Owen scrambling iterator for doing multiple i.i.d. [`OwenScrambling`](@ref) randomization.
 One can use the common[`DesignMatrix`](@ref) interface to create the iterator.
 """
@@ -22,6 +23,7 @@ end
 
 """
     ScrambleDesignMat{T<:Real, I<:Integer} <: AbstractDesignMatrix
+
 Create a scrambling iterator (Digital Shift or Matousek depending on the `R` field) for doing multiple i.i.d. [`DigitalShift`](@ref) or [`MatousekScrambling`](@ref) randomization.
 One can use the common[`DesignMatrix`](@ref) interface to create the iterator.
 """
@@ -35,6 +37,7 @@ end
 
 """
     ShiftDesignMat{T<:Real} <: AbstractDesignMatrix
+
 Create a Shift iterator for doing multiple i.i.d [`Shift`](@ref) randomization.
 One can use the common[`DesignMatrix`](@ref) interface to create the iterator.
 """
@@ -46,6 +49,7 @@ end
 
 """
     DistributionDesignMat{T<:Real} <: AbstractDesignMatrix
+
 Create an iterator for multiple distribution randomization. The distribution is chosen with the field `D`.
 This is equivalent to using `rand!(D, X)` for some matrix `X`.
 One can use the common[`DesignMatrix`](@ref) interface to create the iterator.
@@ -58,6 +62,7 @@ end
 
 """
     RandomDesignMat{T<:Real} <: AbstractDesignMatrix
+
 Create an iterator for multiple uniform randomization. This it similar to [`DistributionDesignMat`](@ref) with the field `D = Uniform()`
 One can use the common[`DesignMatrix`](@ref) interface to create the iterator.
 """
@@ -76,44 +81,47 @@ Base.eltype(::Type{RandomDesignMat{T}}) where {T} = Matrix{T} # TODO one could c
 ```julia
 DesignMatrix(n, d, sample_method::DeterministicSamplingAlgorithm, num_mats, T = Float64)
 ```
+
 Create an iterator for doing multiple i.i.d. randomization over QMC sequences where
-- `num_mats` is the length of the iterator
-- `n` is the number of points to sample.
-- `d` is the dimensionality of the point set in `[0, 1)ᵈ`,
-- `sample_method` is the quasi-Monte Carlo sampling strategy used to create a deterministic point set `out`.
-- `T` is the `eltype` of the point sets. For some QMC methods (Faure, Sobol) this can be `Rational`
-It is now compatible with all scrambling methods and shifting. One can also use it with `Distributions.Sampleable` or `RandomSample`.
+
+  - `num_mats` is the length of the iterator
+  - `n` is the number of points to sample.
+  - `d` is the dimensionality of the point set in `[0, 1)ᵈ`,
+  - `sample_method` is the quasi-Monte Carlo sampling strategy used to create a deterministic point set `out`.
+  - `T` is the `eltype` of the point sets. For some QMC methods (Faure, Sobol) this can be `Rational`
+    It is now compatible with all scrambling methods and shifting. One can also use it with `Distributions.Sampleable` or `RandomSample`.
 """
-function DesignMatrix(N, d, S::DeterministicSamplingAlgorithm, num_mats::Integer, T::DataType = Float64)
+function DesignMatrix(
+        N, d, S::DeterministicSamplingAlgorithm, num_mats::Integer, T::DataType = Float64)
     return DesignMatrix(N, d, S, S.R, num_mats, T)
 end
 
 function DesignMatrix(N,
-    d,
-    S::DeterministicSamplingAlgorithm,
-    R::OwenScramble,
-    num_mats::Integer,
-    T::DataType = Float64)
+        d,
+        S::DeterministicSamplingAlgorithm,
+        R::OwenScramble,
+        num_mats::Integer,
+        T::DataType = Float64)
     X, random_bits, bits, indices = initialize(N, d, S, R, T)
     return OwenDesignMat(X, random_bits, bits, indices, R, num_mats)
 end
 
 function DesignMatrix(N,
-    d,
-    S::DeterministicSamplingAlgorithm,
-    R::ScrambleMethod,
-    num_mats::Integer,
-    T = Float64)
+        d,
+        S::DeterministicSamplingAlgorithm,
+        R::ScrambleMethod,
+        num_mats::Integer,
+        T = Float64)
     X, random_bits, bits = initialize(N, d, S, R, T)
     return ScrambleDesignMat(X, random_bits, bits, R, num_mats)
 end
 
 function DesignMatrix(N,
-    d,
-    S::DeterministicSamplingAlgorithm,
-    R::Shift,
-    num_mats,
-    T::DataType = Float64)
+        d,
+        S::DeterministicSamplingAlgorithm,
+        R::Shift,
+        num_mats,
+        T::DataType = Float64)
     X = initialize(N, d, S, R, T)
     return ShiftDesignMat(X, R, num_mats)
 end
@@ -144,10 +152,10 @@ function initialize(n, d, sampler, R::OwenScramble, T::DataType = Float64)
 end
 
 function scramble!(random_points::AbstractArray{T},
-    random_bits,
-    bits,
-    indices,
-    R::OwenScramble) where {T <: Real}
+        random_bits,
+        bits,
+        indices,
+        R::OwenScramble) where {T <: Real}
     randomize_bits!(random_bits, bits, indices, R)
     for i in CartesianIndices(random_points)
         random_points[i] = bits2unif(T, @view(random_bits[:, i]), R.base)
@@ -170,9 +178,9 @@ function initialize(n, d, sampler, R::ScrambleMethod, T::DataType = Float64)
 end
 
 function scramble!(random_points::AbstractArray{T},
-    random_bits,
-    bits,
-    R::ScrambleMethod) where {T <: Real}
+        random_bits,
+        bits,
+        R::ScrambleMethod) where {T <: Real}
     randomize_bits!(random_bits, bits, R)
     for i in CartesianIndices(random_points)
         random_points[i] = bits2unif(T, @view(random_bits[:, i]), R.base)
@@ -202,31 +210,35 @@ end
 """
 ```julia
 generate_design_matrices(n, d, sample_method::DeterministicSamplingAlgorithm,
-num_mats, T = Float64)
+    num_mats, T = Float64)
 generate_design_matrices(n, d, sample_method::RandomSamplingAlgorithm,
-num_mats, T = Float64)
+    num_mats, T = Float64)
 generate_design_matrices(n, lb, ub, sample_method,
-num_mats = 2)
+    num_mats = 2)
 ```
+
 Create `num_mats` matrices each containing a QMC point set, where:
-- `n` is the number of points to sample.
-- `d` is the dimensionality of the point set in `[0, 1)ᵈ`,
-- `sample_method` is the quasi-Monte Carlo sampling strategy used to create a deterministic point set `out`.
-- `T` is the `eltype` of the point sets. For some QMC methods (Faure, Sobol) this can be `Rational`
-If the bound `lb` and `ub` are specified instead of `d`, the samples will be transformed into the box `[lb, ub]`.
+
+  - `n` is the number of points to sample.
+  - `d` is the dimensionality of the point set in `[0, 1)ᵈ`,
+  - `sample_method` is the quasi-Monte Carlo sampling strategy used to create a deterministic point set `out`.
+  - `T` is the `eltype` of the point sets. For some QMC methods (Faure, Sobol) this can be `Rational`
+    If the bound `lb` and `ub` are specified instead of `d`, the samples will be transformed into the box `[lb, ub]`.
 """
-function generate_design_matrices(n, d, sampler::DeterministicSamplingAlgorithm, num_mats::Integer,
-    T::DataType = Float64)
+function generate_design_matrices(
+        n, d, sampler::DeterministicSamplingAlgorithm, num_mats::Integer,
+        T::DataType = Float64)
     return generate_design_matrices(n, d, sampler, sampler.R, num_mats, T)
 end
 
-function generate_design_matrices(n, d, sampler::RandomSamplingAlgorithm, num_mats::Integer,
-    T::DataType = Float64)
+function generate_design_matrices(
+        n, d, sampler::RandomSamplingAlgorithm, num_mats::Integer,
+        T::DataType = Float64)
     return [sample(n, d, sampler, T) for j in 1:num_mats]
 end
 
 function generate_design_matrices(n, lb, ub, sampler,
-    num_mats = 2)
+        num_mats = 2)
     if n <= 0
         throw(ZeroSamplesError())
     end
@@ -246,11 +258,13 @@ end
 
 """
     generate_design_matrices(n, d, sampler, R::NoRand, num_mats, T = Float64)
+
 `R = NoRand()` produces `num_mats` matrices each containing a different deterministic point set in `[0, 1)ᵈ`.
 Note that this is an ad hoc way to produce i.i.d sequence as it creates a deterministic point in dimension `d × num_mats` and split it in `num_mats` point set of dimension `d`.
 This does not have any QMC garantuees.
 """
-function generate_design_matrices(n, d, sampler, R::NoRand, num_mats::Integer, T::DataType = Float64)
+function generate_design_matrices(
+        n, d, sampler, R::NoRand, num_mats::Integer, T::DataType = Float64)
     out = sample(n, num_mats * d, sampler, T)
     @warn "The `generate_design_matrices(n, d, sampler, R = NoRand(), num_mats)` method does not produces true and independent QMC matrices, see [this doc warning](https://docs.sciml.ai/QuasiMonteCarlo/stable/design_matrix/) for more context.
     Prefer using randomization methods such as `R = Shift()`, `R = MatousekScrambling()`, etc., see [documentation](https://docs.sciml.ai/QuasiMonteCarlo/stable/randomization/)"
@@ -258,10 +272,10 @@ function generate_design_matrices(n, d, sampler, R::NoRand, num_mats::Integer, T
 end
 
 function generate_design_matrices(n,
-    d,
-    sampler,
-    R::RandomizationMethod,
-    num_mats::Integer,
-    T::DataType = Float64)
+        d,
+        sampler,
+        R::RandomizationMethod,
+        num_mats::Integer,
+        T::DataType = Float64)
     return collect(DesignMatrix(n, d, sampler, R, num_mats, T))
 end
diff --git a/src/RandomizedQuasiMonteCarlo/scrambling_base_b.jl b/src/RandomizedQuasiMonteCarlo/scrambling_base_b.jl
index d5bd324..0b6ef0c 100644
--- a/src/RandomizedQuasiMonteCarlo/scrambling_base_b.jl
+++ b/src/RandomizedQuasiMonteCarlo/scrambling_base_b.jl
@@ -51,7 +51,7 @@ Base.@kwdef struct OwenScramble{I <: Integer} <: ScrambleMethod
 end
 
 function randomize!(random_points::AbstractMatrix{T},
-    points::AbstractMatrix{T}, R::OwenScramble) where {T <: Real}
+        points::AbstractMatrix{T}, R::OwenScramble) where {T <: Real}
     @assert size(points) == size(random_points)
     b = R.base
     unrandomized_bits = unif2bits(points, b, pad = R.pad)
@@ -70,9 +70,9 @@ In place version of a `OwenScramble` (Nested Uniform Scramble) for the "bit" arr
 This is faster to use this functions for multiple scramble of the same array (use `generate_design_matrices`).
 """
 function randomize_bits!(random_bits::AbstractArray{T, 3},
-    origin_bits::AbstractArray{T, 3},
-    indices::AbstractArray{F, 3},
-    R::OwenScramble) where {T <: Integer, F <: Integer}
+        origin_bits::AbstractArray{T, 3},
+        indices::AbstractArray{F, 3},
+        R::OwenScramble) where {T <: Integer, F <: Integer}
     # in place nested uniform Scramble.
     #
     m, n, d = size(indices)
@@ -132,7 +132,7 @@ function which_permutation(bits::AbstractArray, b::I) where {I <: Integer}
 end
 
 function which_permutation!(indices::AbstractMatrix{<:Integer},
-    bits::AbstractMatrix{<:Integer}, b::Integer)
+        bits::AbstractMatrix{<:Integer}, b::Integer)
     @assert size(indices)[2:end]==size(bits)[2:end] "You need size(indices) = $(size(indices)) equal size(bits) = $(size(bits))"
     indices[1, :] .= 0 # same permutation for all observations i
     for i in axes(indices, 2)                     # Here is where we want m > 0 so the loop works ok
@@ -144,7 +144,7 @@ function which_permutation!(indices::AbstractMatrix{<:Integer},
 end
 
 function randomize!(random_points::AbstractMatrix{T},
-    points::AbstractMatrix{T}, R::ScrambleMethod) where {T <: Real}
+        points::AbstractMatrix{T}, R::ScrambleMethod) where {T <: Real}
     @assert size(points) == size(random_points)
     b = R.base
     unrandomized_bits = unif2bits(points, b, pad = R.pad)
@@ -182,8 +182,8 @@ In place version of a ScrambleMethod (`MatousekScramble` or `DigitalShift`) for
 This is faster to use this functions for multiple scramble of the same array (use `generate_design_matrices`).
 """
 function randomize_bits!(random_bits::AbstractArray{T, 3},
-    origin_bits::AbstractArray{T, 3},
-    R::MatousekScramble) where {T <: Integer}
+        origin_bits::AbstractArray{T, 3},
+        R::MatousekScramble) where {T <: Integer}
     # https://statweb.stanford.edu/~owen/mc/ Chapter 17.6 around equation (17.15).
     #
     pad, n, d = size(origin_bits)
@@ -247,8 +247,8 @@ Base.@kwdef struct DigitalShift{I <: Integer} <: ScrambleMethod
 end
 
 function randomize_bits!(random_bits::AbstractArray{T, 3},
-    origin_bits::AbstractArray{T, 3},
-    R::DigitalShift) where {T <: Integer}
+        origin_bits::AbstractArray{T, 3},
+        R::DigitalShift) where {T <: Integer}
     # https://statweb.stanford.edu/~owen/mc/ Chapter 17.6 around equation (17.15).
     #
     pad, n, d = size(origin_bits)
diff --git a/src/RandomizedQuasiMonteCarlo/shifting.jl b/src/RandomizedQuasiMonteCarlo/shifting.jl
index c673656..45b8978 100644
--- a/src/RandomizedQuasiMonteCarlo/shifting.jl
+++ b/src/RandomizedQuasiMonteCarlo/shifting.jl
@@ -37,7 +37,7 @@ function shift!(points::AbstractMatrix{T}, U::AbstractVector{T}) where {T <: Rea
 end
 
 function shift!(rng::AbstractRNG, points::AbstractMatrix{T},
-    U::AbstractVector{T}) where {T <: Real}
+        U::AbstractVector{T}) where {T <: Real}
     rand!(rng, U)
     for i in axes(points, 2)
         points[:, i] += U
diff --git a/src/VanDerCorput.jl b/src/VanDerCorput.jl
index ec1d884..d009123 100644
--- a/src/VanDerCorput.jl
+++ b/src/VanDerCorput.jl
@@ -40,7 +40,7 @@ function _vdc(n::I, base::I, F = Float64) where {I <: Integer}
     return _vdc(one(n), I(log(base, n)), base, F; n)
 end
 @fastmath @views function _vdc(λ::I, n_digits::I, base::I, F = Float64;
-    n = λ * base^n_digits) where {I <: Integer}
+        n = λ * base^n_digits) where {I <: Integer}
     sequence = zeros(F, n)
     inv_base = convert(F, inv(base))
     dgs = fill(base - 1, n_digits)
diff --git a/src/net_utilities.jl b/src/net_utilities.jl
index d46f111..9cad53d 100644
--- a/src/net_utilities.jl
+++ b/src/net_utilities.jl
@@ -46,7 +46,7 @@ end
 Convert a vector of pad "bits" in base b into a number y∈[0,1[.
 """
 function bits2unif(::Type{T}, bits::AbstractVector{<:Integer},
-    b::Integer) where {T <: Rational}
+        b::Integer) where {T <: Rational}
     # Turn sequence of bits into a point in [0,1)
     # First bits are highest order
     y = zero(T)
@@ -57,7 +57,7 @@ function bits2unif(::Type{T}, bits::AbstractVector{<:Integer},
 end
 
 function bits2unif(::Type{T}, bits::AbstractVector{<:Integer},
-    b::Integer) where {T <: AbstractFloat}
+        b::Integer) where {T <: AbstractFloat}
     # Turn sequence of bits into a point in [0,1)
     # First bits are highest order
     y = zero(T)
diff --git a/test/rfaure.jl b/test/rfaure.jl
index a11d98a..558fbb5 100644
--- a/test/rfaure.jl
+++ b/test/rfaure.jl
@@ -1,289 +1,289 @@
 [0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647 0.0588235294117647;
-    0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529 0.117647058823529;
-    0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294 0.176470588235294;
-    0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059 0.235294117647059;
-    0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824 0.294117647058824;
-    0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588 0.352941176470588;
-    0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353 0.411764705882353;
-    0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118 0.470588235294118;
-    0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882 0.529411764705882;
-    0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647 0.588235294117647;
-    0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412 0.647058823529412;
-    0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176 0.705882352941176;
-    0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941 0.764705882352941;
-    0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706 0.823529411764706;
-    0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471 0.882352941176471;
-    0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235 0.941176470588235;
-    0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692;
-    0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675;
-    0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214;
-    0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986;
-    0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751;
-    0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516;
-    0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128;
-    0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045;
-    0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481;
-    0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574;
-    0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339;
-    0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104;
-    0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869;
-    0.768166089965398 0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633;
-    0.826989619377163 0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398;
-    0.885813148788927 0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163;
-    0.944636678200692 0.00346020761245675 0.0622837370242214 0.121107266435986 0.179930795847751 0.238754325259516 0.29757785467128 0.356401384083045 0.41522491349481 0.474048442906574 0.532871972318339 0.591695501730104 0.650519031141869 0.709342560553633 0.768166089965398 0.826989619377163 0.885813148788927;
-    0.0069204152249135 0.124567474048443 0.242214532871972 0.359861591695502 0.477508650519031 0.595155709342561 0.71280276816609 0.830449826989619 0.948096885813149 0.0657439446366782 0.183391003460208 0.301038062283737 0.418685121107266 0.536332179930796 0.653979238754325 0.771626297577855 0.889273356401384;
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-    0.889273356401384 0.0069204152249135 0.124567474048443 0.242214532871972 0.359861591695502 0.477508650519031 0.595155709342561 0.71280276816609 0.830449826989619 0.948096885813149 0.0657439446366782 0.183391003460208 0.301038062283737 0.418685121107266 0.536332179930796 0.653979238754325 0.771626297577855;
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-    0.069204152249135 0.245674740484429 0.422145328719723 0.598615916955017 0.775086505190311 0.951557093425606 0.1280276816609 0.304498269896194 0.480968858131488 0.657439446366782 0.833910034602076 0.0103806228373702 0.186851211072664 0.363321799307958 0.539792387543253 0.716262975778547 0.892733564013841;
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+ 0.166089965397924 0.98961937716263 0.813148788927336 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806 0.519031141868512 0.342560553633218;
+ 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806 0.519031141868512 0.342560553633218 0.166089965397924 0.98961937716263 0.813148788927336 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571 0.577854671280277 0.401384083044983;
+ 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806 0.519031141868512 0.342560553633218 0.166089965397924 0.98961937716263 0.813148788927336 0.636678200692042 0.460207612456747;
+ 0.342560553633218 0.166089965397924 0.98961937716263 0.813148788927336 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806 0.519031141868512;
+ 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806 0.519031141868512 0.342560553633218 0.166089965397924 0.98961937716263 0.813148788927336 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571 0.577854671280277;
+ 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806 0.519031141868512 0.342560553633218 0.166089965397924 0.98961937716263 0.813148788927336 0.636678200692042;
+ 0.519031141868512 0.342560553633218 0.166089965397924 0.98961937716263 0.813148788927336 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806;
+ 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806 0.519031141868512 0.342560553633218 0.166089965397924 0.98961937716263 0.813148788927336 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571;
+ 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806 0.519031141868512 0.342560553633218 0.166089965397924 0.98961937716263 0.813148788927336;
+ 0.695501730103806 0.519031141868512 0.342560553633218 0.166089965397924 0.98961937716263 0.813148788927336 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391;
+ 0.754325259515571 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806 0.519031141868512 0.342560553633218 0.166089965397924 0.98961937716263 0.813148788927336 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865;
+ 0.813148788927336 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806 0.519031141868512 0.342560553633218 0.166089965397924 0.98961937716263;
+ 0.8719723183391 0.695501730103806 0.519031141868512 0.342560553633218 0.166089965397924 0.98961937716263 0.813148788927336 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945;
+ 0.930795847750865 0.754325259515571 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806 0.519031141868512 0.342560553633218 0.166089965397924 0.98961937716263 0.813148788927336 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159;
+ 0.98961937716263 0.813148788927336 0.636678200692042 0.460207612456747 0.283737024221453 0.107266435986159 0.930795847750865 0.754325259515571 0.577854671280277 0.401384083044983 0.224913494809689 0.0484429065743945 0.8719723183391 0.695501730103806 0.519031141868512 0.342560553633218 0.166089965397924;
+ 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381;
+ 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145;
+ 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391;
+ 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675;
+ 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439;
+ 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204;
+ 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969;
+ 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734;
+ 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498;
+ 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263;
+ 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028;
+ 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792;
+ 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557;
+ 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322;
+ 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087;
+ 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512;
+ 0.993079584775087 0.875432525951557 0.757785467128028 0.640138408304498 0.522491349480969 0.404844290657439 0.28719723183391 0.169550173010381 0.0519031141868512 0.934256055363322 0.816608996539792 0.698961937716263 0.581314878892734 0.463667820069204 0.346020761245675 0.228373702422145 0.110726643598616;
+ 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073;
+ 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837;
+ 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602;
+ 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367;
+ 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132;
+ 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896;
+ 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661;
+ 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426;
+ 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519;
+ 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955;
+ 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872;
+ 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484;
+ 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249;
+ 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778 0.878892733564014;
+ 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543 0.937716262975778;
+ 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308 0.996539792387543;
+ 0.996539792387543 0.937716262975778 0.878892733564014 0.820069204152249 0.761245674740484 0.70242214532872 0.643598615916955 0.58477508650519 0.525951557093426 0.467128027681661 0.408304498269896 0.349480968858132 0.290657439446367 0.231833910034602 0.173010380622837 0.114186851211073 0.055363321799308;
+ 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0]
diff --git a/test/runtests.jl b/test/runtests.jl
index f18abd8..dbf53a3 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -92,7 +92,7 @@ for point_constructor in [
     LatinHypercubeSample(),
     LatticeRuleSample(),
     RandomSample(),
-    SobolSample(),
+    SobolSample()
 ]
     @show point_constructor
     s = QuasiMonteCarlo.sample(n, lb, ub, point_constructor)
@@ -399,7 +399,7 @@ end
         SobolSample(R = OwenScramble(base = 2, pad = m)),
         SobolSample(),
         LatticeRuleSample(R = Shift()),
-        SobolSample(R = MatousekScramble(base = 2, pad = m)),
+        SobolSample(R = MatousekScramble(base = 2, pad = m))
     ]
     for algorithm in algorithms
         Ms = QuasiMonteCarlo.generate_design_matrices(n, lb, ub, algorithm, num_mat)
@@ -427,7 +427,7 @@ end
         # LatticeRuleSample(R = Shift()), # TODO add support for LatticeRule
         SobolSample(R = OwenScramble(base = 2, pad = m)),
         SobolSample(R = MatousekScramble(base = 2, pad = m)),
-        SobolSample(R = DigitalShift(base = 2, pad = m)),
+        SobolSample(R = DigitalShift(base = 2, pad = m))
     ]
     for algorithm in algorithms
         @show algorithm
@@ -453,8 +453,8 @@ end
         for Int_type in [Int32, Int64]
             b, pad = Int_type(2), Int_type(32)
             scramblings = [OwenScramble(base = b, pad = pad)
-                MatousekScramble(base = b, pad = pad)
-                DigitalShift(base = b, pad = pad)]
+                           MatousekScramble(base = b, pad = pad)
+                           DigitalShift(base = b, pad = pad)]
             for scrambling in scramblings
                 output, other_arrays... = QuasiMonteCarlo.initialize(n,
                     d,
@@ -541,7 +541,7 @@ end
         NoRand(),
         OwenScramble(base = base, pad = m),
         MatousekScramble(base = base, pad = m),
-        DigitalShift(base = base, pad = m),
+        DigitalShift(base = base, pad = m)
     ]
     pass = Array{Bool}(undef, length(v), m)
     for (s, t) in enumerate(t_sobol[1:m])
@@ -568,7 +568,8 @@ end
             base = nextprime(s))
         pass[2, s] = istmsnet(randomize(net, OwenScramble(base = nextprime(s), pad = m));
             λ, t, m, s, base = nextprime(s))
-        pass[3, s] = istmsnet(randomize(net,
+        pass[3, s] = istmsnet(
+            randomize(net,
                 MatousekScramble(base = nextprime(s), pad = m));
             λ, t, m, s, base = nextprime(s))
         pass[4, s] = istmsnet(randomize(net, DigitalShift(base = nextprime(s), pad = m));