diff --git a/Project.toml b/Project.toml
index 47e265e..521b89b 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "FractalDimensions"
 uuid = "4665ce21-e117-4649-aed8-08bbe5ccbead"
 authors = ["George Datseris <datseris.george@gmail.com>", "Ignacio Del Amo <@PythagoreanCult>", "Anton P Braun"]
-version = "1.8.1"
+version = "1.8.2"
 
 [deps]
 ComplexityMeasures = "ab4b797d-85ee-42ba-b621-05d793b346a2"
@@ -19,7 +19,7 @@ StateSpaceSets = "40b095a5-5852-4c12-98c7-d43bf788e795"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 
 [compat]
-ComplexityMeasures = "2.8, 3"
+ComplexityMeasures = "3"
 Distances = "0.10"
 Distributions = "0.24, 0.25"
 HypothesisTests = "0.11"
@@ -30,6 +30,6 @@ Reexport = "1"
 Roots = "1, 2"
 Scratch = "1"
 SpecialFunctions = "1, 2"
-StateSpaceSets = "1"
+StateSpaceSets = "2"
 Statistics = "1"
 julia = "1.5"
diff --git a/docs/make.jl b/docs/make.jl
index 3350d94..17a4422 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -21,5 +21,5 @@ bib = CitationBibliography(
 )
 
 build_docs_with_style(FRACTALDIMENSION_PAGES, FractalDimensions, ComplexityMeasures, StateSpaceSets;
-    expandfirst = ["index.md"], bib,
+    expandfirst = ["index.md"], bib, warnonly = [:cross_references, :doctest, :missing_docs],
 )
diff --git a/docs/src/index.md b/docs/src/index.md
index aaed4bf..cd805e4 100644
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -91,8 +91,16 @@ The whole above pipeline we went through is bundled in [`grassberger_proccacia_d
 ```@docs
 slopefit
 LinearRegression
+linreg
+```
+
+```@docs
 LargestLinearRegion
 linear_regions
+linear_region
+```
+
+```@docs
 AllSlopesDistribution
 estimate_boxsizes
 minimum_pairwise_distance
@@ -100,7 +108,7 @@ minimum_pairwise_distance
 
 ## Generalized (entropy) dimension
 
-Based on the definition of the Generalized entropy ([`genentropy`](@ref)), one can calculate an appropriate dimension, called *generalized dimension*:
+Based on the definition of the generalized (Renyi) entropy, one can calculate an appropriate dimension, called *generalized dimension*:
 ```@docs
 generalized_dim
 molteno_dim
@@ -173,6 +181,7 @@ extremevaltheory_gpdfit_pvalues
 ### Block-maxima estimator
 ```@docs
 BlockMaxima
+estimate_gev_parameters
 ```
 
 ## Theiler window
diff --git a/docs/style.jl b/docs/style.jl
index 8429fd5..691fffa 100644
--- a/docs/style.jl
+++ b/docs/style.jl
@@ -1,4 +1,4 @@
-# %% Color theme definitions
+# Color theme definitions
 struct CyclicContainer <: AbstractVector{String}
     c::Vector{String}
     n::Int
@@ -17,23 +17,22 @@ end
 Base.iterate(c::CyclicContainer, i = 1) = iterate(c.c, i)
 
 COLORSCHEME = [
-    "#6D44D0",
-    "#2CB3BF",
-    "#1B1B1B",
-    "#DA5210",
-    "#03502A",
-    "#866373",
+    "#7143E0",
+    "#191E44",
+    "#0A9A84",
+    "#AF9327",
+    "#791457",
+    "#6C768C",
 ]
 
 COLORS = CyclicContainer(COLORSCHEME)
 LINESTYLES = CyclicContainer(["-", ":", "--", "-."])
 
-# %% Makie styling
 # other styling elements for Makie
 set_theme!(;
     palette = (color = COLORSCHEME,),
     fontsize = 22,
-    figure_padding = 4,
-    resolution = (1000, 500),
+    figure_padding = 8,
+    size = (800, 400),
     linewidth = 3.0,
 )
diff --git a/src/corrsum_based/correlationsum_boxassisted.jl b/src/corrsum_based/correlationsum_boxassisted.jl
index 459bccd..6f01249 100644
--- a/src/corrsum_based/correlationsum_boxassisted.jl
+++ b/src/corrsum_based/correlationsum_boxassisted.jl
@@ -62,7 +62,7 @@ exact to the original [`correlationsum`](@ref). For any other `P`, some
 point pairs that should have been included may be skipped due to having smaller
 distance in the remaining dimensions, but larger distance in the first `P` dimensions.
 """
-function boxed_correlationsum(X; P = 2, kwargs...)
+function boxed_correlationsum(X::AbstractStateSpaceSet; P = 2, kwargs...)
     P = min(P, dimension(X))
     r0, ε0 = estimate_r0_buenoorovio(X, P)
     εs = 2.0 .^ range(log2(ε0), log2(r0); length = 16)
@@ -75,7 +75,7 @@ boxed_correlationsum(X, e::Real, r0 = e; kwargs...) = boxed_correlationsum(X, [e
 function boxed_correlationsum(
         X, εs, r0 = maximum(εs); q = 2, P = 2, kwargs...
     )
-    P ≤ size(X, 2)   || error("Prism dimension has to be ≤ than `X` dimension.")
+    P ≤ dimension(X)   || error("Prism dimension has to be ≤ than `X` dimension.")
     issorted(εs)     || error("Sorted `εs` required for optimized version.")
     if r0 < maximum(εs)
         @warn("Box size `r0` has to be ≥ than `maximum(εs)`.")
@@ -358,7 +358,7 @@ using Random: shuffle!
 
 Estimate a reasonable size for boxing `X`, proposed by
 Bueno-Orovio and Pérez-García [BuenoOrovio2007](@cite), before calculating the correlation
-dimension as presented by [Theiler1983](@cite).
+dimension as presented by [Theiler1987](@cite).
 Return the size `r0` and the minimum interpoint distance `ε0` in the data.
 
 If instead of boxes, prisms
diff --git a/src/corrsum_based/correlationsum_vanilla.jl b/src/corrsum_based/correlationsum_vanilla.jl
index e600303..a8fb70c 100644
--- a/src/corrsum_based/correlationsum_vanilla.jl
+++ b/src/corrsum_based/correlationsum_vanilla.jl
@@ -21,7 +21,7 @@ i.e. it calculates [`correlationsum`](@ref) for various radii and then tries to
 a linear region in the plot of the log of the correlation sum versus log(ε).
 
 See [`correlationsum`](@ref) for the available keywords.
-See also [`takens_best_estimate`](@ref), [`boxassisted_correlation_dim`](@ref).
+See also [`takens_best_estimate_dim`](@ref), [`boxassisted_correlation_dim`](@ref).
 """
 function grassberger_proccacia_dim(X::AbstractStateSpaceSet, εs = estimate_boxsizes(X); kwargs...)
     cm = correlationsum(X, εs; kwargs...)
diff --git a/src/extremes_based/confidence.jl b/src/extremes_based/confidence.jl
index 6b59c00..9471eea 100644
--- a/src/extremes_based/confidence.jl
+++ b/src/extremes_based/confidence.jl
@@ -113,7 +113,7 @@ function gpd_nrmse(E, gpd, nbins)
     # Compute histogram of E
     bins = range(0, nextfloat(maximum(E), 2); length = nbins)
     binning = FixedRectangularBinning(bins)
-    allprobs = allprobabilities(ValueHistogram(binning), E)
+    allprobs = allprobabilities_and_outcomes(ValueHistogram(binning), E)[1]
     width = step(bins)
 
     # We will calcuate the GPD pdf at the midpoint of each bin
diff --git a/src/extremes_based/gpd.jl b/src/extremes_based/gpd.jl
index 983ae74..f713925 100644
--- a/src/extremes_based/gpd.jl
+++ b/src/extremes_based/gpd.jl
@@ -63,7 +63,7 @@ Estimate and return the parameters `σ, ξ` of a Generalized Pareto Distribution
 fit to `X` (which typically is the exceedances of the log distance of a state space set),
 assuming that `minimum(X) ≥ 0` and hence the parameter `μ` is 0
 (if not, simply shift `X` by its minimum), according to the methods provided
-in [Pons2023](@ref).
+in [Pons2023](@cite).
 
 The estimator can be:
 
diff --git a/src/linear_fits/estimate_boxsizes.jl b/src/linear_fits/estimate_boxsizes.jl
index b50b227..d7a3a64 100644
--- a/src/linear_fits/estimate_boxsizes.jl
+++ b/src/linear_fits/estimate_boxsizes.jl
@@ -4,7 +4,7 @@ export estimate_boxsizes
     estimate_boxsizes(X::AbstractStateSpaceSet; kwargs...) → εs
 
 Return `k` exponentially spaced values: `εs = base .^ range(lower + w, upper + z; length = k)`,
-that are a good estimate for sizes ε that are used in calculating a [Fractal Dimension](@ref).
+that are a good estimate for sizes ε that are used in calculating a fractal Dimension.
 It is strongly recommended to [`standardize`](@ref) input dataset before using this
 function.