From bc24f1a4a26bb2ebcfb86589118a025533197f19 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20=C5=9Al=C4=99zak?=
 <128084860+jaksle@users.noreply.github.com>
Date: Mon, 15 Jan 2024 20:15:37 +0100
Subject: [PATCH 1/3] Update univariate.jl

---
 src/univariate.jl | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/src/univariate.jl b/src/univariate.jl
index cba5181d..d9dae572 100644
--- a/src/univariate.jl
+++ b/src/univariate.jl
@@ -147,6 +147,20 @@ function conv(k::UnivariateKDE, dist::UnivariateDistribution)
 end
 
 # main kde interface methods
+
+"""
+
+	kde(data; [options])
+	kde((xdata, ydata); [options])
+	
+Kernel density estimation method. Returns 1D or 2D KDE object. The grid used and the values of the estimated density can be obtained from fields `.x` and `.density` respectively. To obtain kde values at points different than the initial grid use the `pdf` method.
+
+The optional keyword arguments are
+* `boundary`: the lower and upper limits of the kde, tuple in 1D case, tuple of tuples in 2D case, 
+* `npoints`: the number of interpolation points to use,
+* `kernel = Normal`: the distributional family from [Distributions.jl](https://github.com/JuliaStats/Distributions.jl),
+* `bandwidth`: the bandwidth of the kernel; default is calculated using Silverman's rule. 
+"""
 function kde(data::AbstractVector{<:Real}, weights::Weights, midpoints::R, dist::UnivariateDistribution) where R<:AbstractRange
     k = tabulate(data, midpoints, weights)
     conv(k,dist)

From d46dd064503f505e61542d7b3465d05bfad4696b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20=C5=9Al=C4=99zak?=
 <128084860+jaksle@users.noreply.github.com>
Date: Tue, 16 Jan 2024 13:36:20 +0100
Subject: [PATCH 2/3] Update src/univariate.jl

Co-authored-by: Tamas K. Papp <tkpapp@gmail.com>
---
 src/univariate.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/univariate.jl b/src/univariate.jl
index d9dae572..242f1a92 100644
--- a/src/univariate.jl
+++ b/src/univariate.jl
@@ -155,7 +155,7 @@ end
 	
 Kernel density estimation method. Returns 1D or 2D KDE object. The grid used and the values of the estimated density can be obtained from fields `.x` and `.density` respectively. To obtain kde values at points different than the initial grid use the `pdf` method.
 
-The optional keyword arguments are
+The keyword arguments are
 * `boundary`: the lower and upper limits of the kde, tuple in 1D case, tuple of tuples in 2D case, 
 * `npoints`: the number of interpolation points to use,
 * `kernel = Normal`: the distributional family from [Distributions.jl](https://github.com/JuliaStats/Distributions.jl),

From ccbba39b4f1655bb8fe5b803aa2243109dba834d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jakub=20=C5=9Al=C4=99zak?=
 <128084860+jaksle@users.noreply.github.com>
Date: Tue, 16 Jan 2024 13:36:26 +0100
Subject: [PATCH 3/3] Update src/univariate.jl

Co-authored-by: Tamas K. Papp <tkpapp@gmail.com>
---
 src/univariate.jl | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/univariate.jl b/src/univariate.jl
index 242f1a92..5f71a809 100644
--- a/src/univariate.jl
+++ b/src/univariate.jl
@@ -150,8 +150,8 @@ end
 
 """
 
-	kde(data; [options])
-	kde((xdata, ydata); [options])
+	kde(data; kwargs...)
+	kde((xdata, ydata); kwargs...)
 	
 Kernel density estimation method. Returns 1D or 2D KDE object. The grid used and the values of the estimated density can be obtained from fields `.x` and `.density` respectively. To obtain kde values at points different than the initial grid use the `pdf` method.