From 6ac503848b7478ab5985f16559ad4a67302e1cc7 Mon Sep 17 00:00:00 2001
From: Santymax98 <132070198+Santymax98@users.noreply.github.com>
Date: Fri, 23 Feb 2024 10:39:13 -0300
Subject: [PATCH 1/4] Update paper.md

add example
---
 joss/paper.md | 29 +++++++++++++++++++++++++++++
 1 file changed, 29 insertions(+)

diff --git a/joss/paper.md b/joss/paper.md
index 6dd598e86..b901f449a 100644
--- a/joss/paper.md
+++ b/joss/paper.md
@@ -108,6 +108,35 @@ using Turing
   Turing.Turing.@addlogprob! loglikelihood(D, dataset)
 end
 ```
+Outside the common context we can use the API to work on different aspects, suppose you want to generate random samples of a copula whose parameter depends on a covariate $X$ [@abegaz2012], for example, Consider the Frank copula whose parameter depends on a covariate $X$ that follows a truncated normal distribution with mean zero and variance 9. Also, consider the parameter $\theta(x), x \in [-2,2]$.
+```julia
+using Distributions, Copulas, DataFrames
+
+x= truncated(Normal(0,3),-2,2) 
+
+function model_1(x)
+   return (10-1.5x^2)
+end
+
+data = rand(x,10^5)
+
+#Frank Copula whose parameter depends on a covariate
+function rFrank(x, C_function, dimension)
+  data_list = DataFrame(u1=Float64[], u2=Float64[])
+  
+  for i in x
+    param = C_function(i)
+    copula_instance = FrankCopula(dimension, param)
+    value_copula = rand(copula_instance, 1)
+    push!(data_list, (value_copula[1], value_copula[2]))
+  end
+
+  return data_list
+end
+
+New_frank = rFrank(data, model_1, 2)
+```
+In this way we easily obtain random samples of a new bivariate Frank copula whose parameter can depend on any covariate.
 
 ## The Archimedean interface
 

From c6e0f303713a8d23d22c6ddaa6ca1859361393a0 Mon Sep 17 00:00:00 2001
From: Santymax98 <132070198+Santymax98@users.noreply.github.com>
Date: Fri, 23 Feb 2024 10:41:51 -0300
Subject: [PATCH 2/4] Update paper.bib

add reference
---
 joss/paper.bib | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/joss/paper.bib b/joss/paper.bib
index 2d1325de5..3323ae07f 100644
--- a/joss/paper.bib
+++ b/joss/paper.bib
@@ -237,4 +237,14 @@ @Article{BenchmarkTools
 primaryClass = {cs.PF},
 adsurl = {https://ui.adsabs.harvard.edu/abs/2016arXiv160804295C},
 adsnote = {Provided by the SAO/NASA Astrophysics Data System}
-}
\ No newline at end of file
+}
+
+@Article{abegaz2012,
+  title= "{Semiparametric estimation of conditional copulas}",
+  author = {Abegaz, Fentaw and Gijbels, Ir{\`e}ne and Veraverbeke, No{\"e}l},
+  journal = {Journal of Multivariate Analysis},
+  volume = {110},
+  pages = {43--73},
+  year = {2012},
+  publisher = {Elsevier}
+}

From ce819ddbe8585072800b5b8b23b6531795014df0 Mon Sep 17 00:00:00 2001
From: Santymax98 <132070198+Santymax98@users.noreply.github.com>
Date: Fri, 23 Feb 2024 19:03:12 -0300
Subject: [PATCH 3/4] Update paper.md

---
 joss/paper.md | 30 ------------------------------
 1 file changed, 30 deletions(-)

diff --git a/joss/paper.md b/joss/paper.md
index b901f449a..38e245a1b 100644
--- a/joss/paper.md
+++ b/joss/paper.md
@@ -108,36 +108,6 @@ using Turing
   Turing.Turing.@addlogprob! loglikelihood(D, dataset)
 end
 ```
-Outside the common context we can use the API to work on different aspects, suppose you want to generate random samples of a copula whose parameter depends on a covariate $X$ [@abegaz2012], for example, Consider the Frank copula whose parameter depends on a covariate $X$ that follows a truncated normal distribution with mean zero and variance 9. Also, consider the parameter $\theta(x), x \in [-2,2]$.
-```julia
-using Distributions, Copulas, DataFrames
-
-x= truncated(Normal(0,3),-2,2) 
-
-function model_1(x)
-   return (10-1.5x^2)
-end
-
-data = rand(x,10^5)
-
-#Frank Copula whose parameter depends on a covariate
-function rFrank(x, C_function, dimension)
-  data_list = DataFrame(u1=Float64[], u2=Float64[])
-  
-  for i in x
-    param = C_function(i)
-    copula_instance = FrankCopula(dimension, param)
-    value_copula = rand(copula_instance, 1)
-    push!(data_list, (value_copula[1], value_copula[2]))
-  end
-
-  return data_list
-end
-
-New_frank = rFrank(data, model_1, 2)
-```
-In this way we easily obtain random samples of a new bivariate Frank copula whose parameter can depend on any covariate.
-
 ## The Archimedean interface
 
 Archimedean copulas form a large class of copulas that has seen a lot of theoretical work. Among others, you may take a look at [@mcneilMultivariateArchimedeanCopulas2009b]. We use [`WilliamsonTransforms.jl`](https://github.com/lrnv/WilliamsonTransforms.jl/)'s implementation of the Williamson $d$-transfrom to sample from any archimedean copula, including for example the `ClaytonCopula` with negative dependence parameter in any dimension, which is a first to our knowledge.

From 728b1d254607421fd6c2eed13a0db86757b2823c Mon Sep 17 00:00:00 2001
From: CompatHelper Julia <compathelper_noreply@julialang.org>
Date: Sun, 25 Feb 2024 00:51:35 +0000
Subject: [PATCH 4/4] CompatHelper: bump compat for TaylorSeries to 0.17, (keep
 existing compat)

---
 Project.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Project.toml b/Project.toml
index a9fe397e9..560cd26d2 100644
--- a/Project.toml
+++ b/Project.toml
@@ -38,7 +38,7 @@ Roots = "1, 2"
 SpecialFunctions = "2"
 StableRNGs = "1"
 StatsBase = "0.33, 0.34"
-TaylorSeries = "0.12, 0.13, 0.14, 0.15, 0.16"
+TaylorSeries = "0.12, 0.13, 0.14, 0.15, 0.16, 0.17"
 Test = "1.6"
 TestItemRunner = "v0.2"
 WilliamsonTransforms = "0.1"