diff --git a/Project.toml b/Project.toml
index 3b3416b..ca46f6e 100644
--- a/Project.toml
+++ b/Project.toml
@@ -5,14 +5,30 @@ version = "0.1.0"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
+Optim = "429524aa-4258-5aef-a3af-852621145aeb"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 StatsAPI = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsModels = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
 SurvivalBase = "9cb6079b-e021-4662-8a75-9f65bfa286f2"
+SurvivalDistributions = "7121986e-ae89-4673-ae4e-1cb822919a18"
 
 [compat]
 Aqua = "0.8"
 DataFrames = "1"
+Distributions = "0.25"
+LinearAlgebra = "1"
+LogExpFunctions = "0.3"
+Optim = "1"
+RDatasets = "0.7"
+Random = "1.6"
+SpecialFunctions = "2"
+StableRNGs = "1"
 StatsAPI = "1"
 StatsBase = "0.34"
 StatsModels = "0.7"
@@ -23,8 +39,11 @@ julia = "1.6.7"
 
 [extras]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
+RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestItemRunner = "f8b46487-2199-4994-9208-9a1283c18c0a"
 
 [targets]
-test = ["Aqua", "Test", "TestItemRunner"]
+test = ["Aqua", "RDatasets", "Random", "Test", "TestItemRunner", "StableRNGs"]
diff --git a/docs/Project.toml b/docs/Project.toml
index 5b0eb8a..9a9ceb6 100644
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -1,3 +1,5 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DocumenterCitations = "daee34ce-89f3-4625-b898-19384cb65244"
+LiveServer = "16fef848-5104-11e9-1b77-fb7a48bbb589"
 SurvivalModels = "230d35e3-bb2c-48f4-adfc-0b42c4b39395"
diff --git a/docs/make.jl b/docs/make.jl
index 6500e32..4bad77e 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -1,10 +1,18 @@
 using SurvivalModels
 using Documenter
+using DocumenterCitations
 
 DocMeta.setdocmeta!(SurvivalModels, :DocTestSetup, :(using SurvivalModels); recursive=true)
 
+bib = CitationBibliography(
+    joinpath(@__DIR__,"src","assets","references.bib"),
+    style=:numeric
+)
+
+
 makedocs(;
     modules=[SurvivalModels],
+    plugins=[bib],
     authors="Oskar Laverny <oskar.laverny@univ-amu.fr> and contributors",
     sitename="SurvivalModels.jl",
     format=Documenter.HTML(;
@@ -14,6 +22,8 @@ makedocs(;
     ),
     pages=[
         "Home" => "index.md",
+        "GeneralHazard.md",
+        "references.md",
     ],
 )
 
diff --git a/docs/src/GeneralHazard.md b/docs/src/GeneralHazard.md
new file mode 100644
index 0000000..268cf41
--- /dev/null
+++ b/docs/src/GeneralHazard.md
@@ -0,0 +1,408 @@
+# Some hazard functions in Julia
+
+# Hazard and cumulative hazard functions
+
+The hazard and the cumulative hazard functions play a crucial role in survival analysis. These functions define the likelihood function in the presence of censored observations. Thus, they are important in many context. For more information about these functions, see [Short course on Parametric Survival Analysis
+](https://github.com/FJRubio67/ShortCourseParamSurvival).
+
+This tutorial contains the implementation of the hazard and cumulative hazard functions for some popular distributions using the Julia package `HazReg.jl`, such as the LogNormal, LogLogistic, Weibull, and Gamma distributions. 
+
+The Julia implementation of other, less common, distributions that are of interest in survival analysis can be found in the Julia package `HazReg.jl`: [`HazReg.jl`](https://github.com/FJRubio67/HazReg.jl).
+
+See also: 
+
+- [HazReg.jl Julia Package](https://github.com/FJRubio67/HazReg.jl) 
+
+# Required packages
+```@example 1
+using Distributions
+using Plots
+using StatsBase
+using HazReg
+```
+
+# Examples
+
+## LogNormal
+
+```@example 1
+#= Hazard function =#
+plot(t -> hazard(LogNormal(0.5, 1), t),
+      xlabel = "x", ylabel = "Hazard", title = "LogNormal distribution",
+    xlims = (0,10),   xticks = 0:1:10, label = "", 
+    xtickfont = font(16, "Courier"),  ytickfont = font(16, "Courier"),
+    xguidefontsize=18, yguidefontsize=18, linewidth=3,
+    linecolor = "blue")
+```
+
+```@example 1
+#= Cumulative Hazard function =#
+plot(t -> cumhazard(LogNormal(0.5, 1),t),
+      xlabel = "x", ylabel = "Cumulative Hazard", title = "LogNormal distribution",
+    xlims = (0,10),   xticks = 0:1:10, label = "", 
+    xtickfont = font(16, "Courier"),  ytickfont = font(16, "Courier"),
+    xguidefontsize=18, yguidefontsize=18, linewidth=3,
+    linecolor = "blue")
+```
+
+
+## LogLogistic
+
+```@example 1
+#= Hazard function =#
+plot(t -> hazard(LogLogistic(1, 0.5),t),
+      xlabel = "x", ylabel = "Hazard", title = "LogLogistic distribution",
+    xlims = (0,10),   xticks = 0:1:10, label = "", 
+    xtickfont = font(16, "Courier"),  ytickfont = font(16, "Courier"),
+    xguidefontsize=18, yguidefontsize=18, linewidth=3,
+    linecolor = "blue")
+```
+
+```@example 1
+#= Cumulative Hazard function =#
+plot(t -> cumhazard(LogLogistic(1, 0.5),t),
+      xlabel = "x", ylabel = "Cumulative Hazard", title = "LogLogistic distribution",
+    xlims = (0,10),   xticks = 0:1:10, label = "", 
+    xtickfont = font(16, "Courier"),  ytickfont = font(16, "Courier"),
+    xguidefontsize=18, yguidefontsize=18, linewidth=3,
+    linecolor = "blue")
+```
+
+## Weibull
+
+```@example 1
+#= Weibull function =#
+plot(t -> hazard(Weibull(3, 0.5),t),
+      xlabel = "x", ylabel = "Hazard", title = "Weibull distribution",
+    xlims = (0,10),   xticks = 0:1:10, label = "", 
+    xtickfont = font(16, "Courier"),  ytickfont = font(16, "Courier"),
+    xguidefontsize=18, yguidefontsize=18, linewidth=3,
+    linecolor = "blue")
+```
+
+```@example 1
+#= Cumulative Hazard function =#
+plot(t -> cumhazard(Weibull(3, 0.5),t),
+      xlabel = "x", ylabel = "Cumulative Hazard", title = "Weibull distribution",
+    xlims = (0,10),   xticks = 0:1:10, label = "", 
+    xtickfont = font(16, "Courier"),  ytickfont = font(16, "Courier"),
+    xguidefontsize=18, yguidefontsize=18, linewidth=3,
+    linecolor = "blue")
+```
+
+
+## Gamma
+
+```@example 1
+#= Weibull function =#
+plot(t -> hazard(Gamma(3, 0.5),t),
+      xlabel = "x", ylabel = "Hazard", title = "Gamma distribution",
+    xlims = (0,10),   xticks = 0:1:10, label = "", 
+    xtickfont = font(16, "Courier"),  ytickfont = font(16, "Courier"),
+    xguidefontsize=18, yguidefontsize=18, linewidth=3,
+    linecolor = "blue")
+```
+
+```@example 1
+#= Cumulative Hazard function =#
+plot(t -> cumhazard(Gamma(3, 0.5),t),
+      xlabel = "x", ylabel = "Cumulative Hazard", title = "Gamma distribution",
+    xlims = (0,10),   xticks = 0:1:10, label = "", 
+    xtickfont = font(16, "Courier"),  ytickfont = font(16, "Courier"),
+    xguidefontsize=18, yguidefontsize=18, linewidth=3,
+    linecolor = "blue")
+```
+
+
+
+# Julia simGH: simulating times to event from a general hazard structure
+
+# The `simGH` command.
+The simGH command from the `HazReg.jl` Julia package allows one to simulate times to event from the following models:
+
+- General Hazard (GH) model [chen:2001](@cite) [rubio:2019](@cite).
+
+- Accelerated Failure Time (AFT) model [kalbfleisch:2011](@cite).
+
+- Proportional Hazards (PH) model [cox:1972](@cite).
+
+- Accelerated Hazards (AH) model [chen:2000](@cite).
+
+
+A description of these hazard models is presented below as well as the available baseline hazards.
+
+## General Hazard model
+The GH model is formulated in terms of the hazard structure
+
+$$h(t; \alpha, \beta, \theta, {\bf x}) = h_0\left(t  \exp\{\tilde{\bf x}^{\top}\alpha\}; \theta\right) \exp\{{\bf x}^{\top}\beta\}.$$
+
+where ${\bf x}\in{\mathbb R}^p$ are the covariates that affect the hazard level; $\tilde{\bf x} \in {\mathbb R}^q$ are the covariates the affect the time level (typically $\tilde{\bf x} \subset {\bf x}$); $\alpha \in {\mathbb R}^q$ and $\beta \in {\mathbb R}^p$ are the regression coefficients; and $\theta \in \Theta$ is the vector of parameters of the baseline hazard $h_0(\cdot)$.
+
+This hazard structure leads to an identifiable model as long as the baseline hazard is not a hazard associated to a member of the Weibull family of distributions [chen:2001](@cite). 
+
+## Accelerated Failure Time (AFT) model
+The AFT model is formulated in terms of the hazard structure
+
+$$h(t; \beta, \theta, {\bf x}) = h_0\left(t  \exp\{{\bf x}^{\top}\beta\}; \theta\right) \exp\{{\bf x}^{\top}\beta\}.$$
+
+where ${\bf x}\in{\mathbb R}^p$ are the available covariates; $\beta \in {\mathbb R}^p$ are the regression coefficients; and $\theta \in \Theta$ is the vector of parameters of the baseline hazard $h_0(\cdot)$.
+
+## Proportional Hazards (PH) model
+The PH model is formulated in terms of the hazard structure
+
+$$h(t; \beta, \theta, {\bf x}) = h_0\left(t ; \theta\right) \exp\{{\bf x}^{\top}\beta\}.$$
+
+where ${\bf x}\in{\mathbb R}^p$ are the available covariates; $\beta \in {\mathbb R}^p$ are the regression coefficients; and $\theta \in \Theta$ is the vector of parameters of the baseline hazard $h_0(\cdot)$.
+
+## Accelerated Hazards (AH) model
+The AH model is formulated in terms of the hazard structure
+
+$$h(t; \alpha, \theta, \tilde{\bf x}) = h_0\left(t \exp\{\tilde{\bf x}^{\top}\alpha\}; \theta\right) .$$
+
+where $\tilde{\bf x}\in{\mathbb R}^q$ are the available covariates; $\alpha \in {\mathbb R}^q$ are the regression coefficients; and $\theta \in \Theta$ is the vector of parameters of the baseline hazard $h_0(\cdot)$.
+
+# Available baseline hazards
+The current version of the `simGH` command implements the following parametric baseline hazards for the models discussed in the previous section.
+
+- [Power Generalised Weibull](http://rpubs.com/FJRubio/PGW) (PGW) distribution.
+
+- [Exponentiated Weibull](http://rpubs.com/FJRubio/EWD) (EW) distribution.
+
+- [Generalised Gamma](http://rpubs.com/FJRubio/GG) (GenGamma) distribuiton.
+
+- [Gamma](https://en.wikipedia.org/wiki/Gamma_distribution) (Gamma) distribution.
+
+- [Lognormal](https://en.wikipedia.org/wiki/Log-normal_distribution) (LogNormal) distribution.
+
+- [Log-logistic](https://en.wikipedia.org/wiki/Log-logistic_distribution) (LogLogistic) distribution.
+
+- [Weibull](https://en.wikipedia.org/wiki/Weibull_distribution) (Weibull) distribution. (only for AFT, PH, and AH models)
+
+
+# Illustrative example: Julia code
+In this example, we simulate $n=1,000$ times to event from the GH, PH, AFT, and AH models with PGW baseline hazards, using the Julia `simGH` command from the `HazReg` package. We censor these samples at a fixed value, and fit the corresponding models using the Julia package `HazReg`.
+
+See also: 
+
+- [HazReg.jl Julia Package](https://github.com/FJRubio67/HazReg.jl) 
+
+## PGW-GH model
+
+```@example 1
+# Required packages
+using HazReg
+using Distributions
+using Random
+using DataFrames
+using Optim
+
+# Sample size
+n = 10000
+
+# Simulated design matrices
+Random.seed!(123)
+dist = Normal()
+des = hcat(rand(dist, n), rand(dist, n))
+des_t = rand(dist, n)
+
+
+#----------------------------
+# PGW-GH simulation
+#----------------------------
+
+# True parameters
+theta0 = [0.1,2.0,5.0]
+alpha0 = 0.5
+beta0 = [-0.5,0.75]
+
+# censoring
+cens = 10
+
+# Data simulation
+simdat = simGH(seed = 1234, n = n, des = des, des_t = des_t,
+      alpha = alpha0, beta = beta0, 
+      hstr = "GH", dist = PowerGeneralizedWeibull(theta0...))
+
+# status variable
+status = collect(Bool,(simdat .< cens))
+
+# Inducing censoring
+simdat = min.(simdat, cens)
+
+# Model fit
+OPTPGWGH = GHMLE(init = fill(0.0, 3 + 1 + size(des)[2]), times = simdat,
+            status = status, hstr = "GH", dist = PowerGeneralizedWeibull, 
+            des = des, des_t = des_t, method = NelderMead(), maxit = 1000)
+
+MLEPGWGH = [exp(OPTPGWGH[1].minimizer[j]) for j in 1:3] 
+append!(MLEPGWGH, OPTPGWGH[1].minimizer[4:end]) 
+
+# True parameter values vs. MLE
+COMP =  hcat(vcat(theta0,alpha0,beta0),MLEPGWGH);  
+COMP = DataFrame(COMP, :auto);
+ 
+rename!( COMP, ["True", "MLE"] );
+println(COMP)
+```
+
+
+
+## PGW-PH model
+
+```@example 1
+
+# Sample size
+n = 10000
+
+# Simulated design matrices
+Random.seed!(123)
+dist = Normal()
+des = hcat(rand(dist, n), rand(dist, n))
+
+
+#----------------------------
+# PGW-PH simulation
+#----------------------------
+
+# True parameters
+theta0 = [0.1,2.0,5.0]
+beta0 = [-0.5,0.75]
+
+# censoring
+cens = 10
+
+# Data simulation
+simdat = simGH(seed = 1234, n = n, des = des, des_t = nothing,
+      alpha = nothing, beta = beta0, 
+      hstr = "PH", dist = PowerGeneralizedWeibull(theta0...))
+
+# status variable
+status = collect(Bool,(simdat .< cens))
+
+# Inducing censoring
+simdat = min.(simdat, cens)
+
+# Model fit
+OPTPGWPH = GHMLE(init = fill(0.0, 3 + size(des)[2]), times = simdat,
+            status = status, hstr = "PH", dist = PowerGeneralizedWeibull, 
+            des = des, des_t = nothing, method = NelderMead(), maxit = 1000)
+
+MLEPGWPH = [exp(OPTPGWPH[1].minimizer[j]) for j in 1:3] 
+append!(MLEPGWPH, OPTPGWPH[1].minimizer[4:end]) 
+
+# True parameter values vs. MLE
+COMP =  hcat(vcat(theta0,beta0),MLEPGWPH);  
+COMP = DataFrame(COMP, :auto);
+ 
+rename!( COMP, ["True", "MLE"] );
+println(COMP)
+```
+
+
+## PGW-AFT model
+
+```@example 1
+# Sample size
+n = 10000
+
+# Simulated design matrices
+Random.seed!(123)
+dist = Normal()
+des = hcat(rand(dist, n), rand(dist, n))
+
+
+#----------------------------
+# PGW-AFT simulation
+#----------------------------
+
+# True parameters
+theta0 = [0.1,2.0,5.0]
+beta0 = [-0.5,0.75]
+
+# censoring
+cens = 10
+
+# Data simulation
+simdat = simGH(seed = 1234, n = n, des = des, des_t = nothing,
+      alpha = nothing, beta = beta0, 
+      hstr = "AFT", dist = PowerGeneralizedWeibull(theta0...))
+
+# status variable
+status = collect(Bool,(simdat .< cens))
+
+# Inducing censoring
+simdat = min.(simdat, cens)
+
+# Model fit
+OPTPGWAFT = GHMLE(init = fill(0.0, 3 + size(des)[2]), times = simdat,
+            status = status, hstr = "AFT", dist = PowerGeneralizedWeibull, 
+            des = des, des_t = nothing, method = NelderMead(), maxit = 1000)
+
+MLEPGWAFT = [exp(OPTPGWAFT[1].minimizer[j]) for j in 1:3] 
+append!(MLEPGWAFT, OPTPGWAFT[1].minimizer[4:end]) 
+
+# True parameter values vs. MLE
+COMP =  hcat(vcat(theta0,beta0),MLEPGWAFT);  
+COMP = DataFrame(COMP, :auto);
+ 
+rename!( COMP, ["True", "MLE"] );
+println(COMP)
+```
+
+
+
+## PGW-AH model
+
+```@example 1
+
+# Sample size
+n = 10000
+
+# Simulated design matrices
+Random.seed!(123)
+dist = Normal()
+des_t = hcat(rand(dist, n), rand(dist, n))
+
+
+#----------------------------
+# PGW-AH simulation
+#----------------------------
+
+# True parameters
+theta0 = [0.1,2.0,5.0]
+alpha0 = [-0.5,0.75]
+
+# censoring
+cens = 10
+
+# Data simulation
+simdat = simGH(seed = 1234, n = n, des = nothing, des_t = des_t,
+      alpha = alpha0, beta = nothing, 
+      hstr = "AH", dist = PowerGeneralizedWeibull(theta0...))
+
+# status variable
+status = collect(Bool,(simdat .< cens))
+
+# Inducing censoring
+simdat = min.(simdat, cens)
+
+# Model fit
+OPTPGWAH = GHMLE(init = fill(0.0, 3 + size(des)[2]), times = simdat,
+            status = status, hstr = "AH", dist = PowerGeneralizedWeibull, 
+            des = nothing, des_t = des_t, method = NelderMead(), maxit = 1000)
+
+MLEPGWAH = [exp(OPTPGWAH[1].minimizer[j]) for j in 1:3] 
+append!(MLEPGWAH, OPTPGWAH[1].minimizer[4:end]) 
+
+# True parameter values vs. MLE
+COMP =  hcat(vcat(theta0,beta0),MLEPGWAH);  
+COMP = DataFrame(COMP, :auto);
+ 
+rename!( COMP, ["True", "MLE"] );
+println(COMP)
+```
+
+```@bibliography
+Pages = ["simGHJulia.md"]
+Canonical = false
+```
\ No newline at end of file
diff --git a/docs/src/assets/LeukSurv.csv b/docs/src/assets/LeukSurv.csv
new file mode 100644
index 0000000..cd93f62
--- /dev/null
+++ b/docs/src/assets/LeukSurv.csv
@@ -0,0 +1,1044 @@
+"","time","cens","xcoord","ycoord","age","sex","wbc","tpi","district"
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+"22",1,1,0.463065049614112,0.208379272326351,88,1,350,1.78,20
+"23",1,1,0.213891951488423,0.502756339581036,78,1,0,3.1,9
+"24",1,1,0.412348401323043,0.423373759647189,44,1,281,4.87,1
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+"26",1,1,0.721058434399118,0.0441014332965821,74,1,147,-1.33,21
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+"28",2,1,0.0760749724366042,0.470782800441014,73,0,147,-2.74,5
+"29",2,1,0.632855567805954,0.191841234840132,78,0,500,2.78,17
+"30",2,1,0.565600882028666,0.240352811466373,84,1,91.7,-1.45,16
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+"32",2,1,0.572216097023153,0.15986769570011,77,0,150,7.86,20
+"33",2,1,0.229327453142227,0.479603087100331,42,0,3.2,-3.38,12
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+"1041",4922,0,0.294377067254686,0.507166482910695,78,0,115,8.06,9
+"1042",4965,1,0.0452039691289967,0.541345093715546,63,1,12.6,1.13,2
+"1043",4977,0,0.561190738699008,0.173098125689085,21,1,13.5,1.63,20
diff --git a/docs/src/assets/citations.css b/docs/src/assets/citations.css
new file mode 100644
index 0000000..89edb15
--- /dev/null
+++ b/docs/src/assets/citations.css
@@ -0,0 +1,17 @@
+.citation dl {
+    display: grid;
+    grid-template-columns: max-content auto; }
+  .citation dt {
+    grid-column-start: 1; }
+  .citation dd {
+    grid-column-start: 2;
+    margin-bottom: 0.75em; }
+  .citation ul {
+   padding: 0 0 2.25em 0;
+   margin: 0;
+   list-style: none;}
+  .citation ul li {
+   text-indent: -2.25em;
+   margin: 0.33em 0.5em 0.5em 2.25em;}
+  .citation ol li {
+   padding-left:0.75em;}
\ No newline at end of file
diff --git a/docs/src/assets/references.bib b/docs/src/assets/references.bib
new file mode 100644
index 0000000..38cff12
--- /dev/null
+++ b/docs/src/assets/references.bib
@@ -0,0 +1,85 @@
+@article{cox1972regression,
+  title={Regression models and life-tables},
+  author={Cox, David R},
+  journal={Journal of the Royal Statistical Society: Series B (Methodological)},
+  volume={34},
+  number={2},
+  pages={187--202},
+  year={1972},
+  publisher={Wiley Online Library}
+}
+
+@article{rubio:2019,
+  title={On a general structure for hazard-based regression models: an application to population-based cancer research},
+  author={Rubio, F.J. and Remontet, L. and Jewell, N.P. and Belot, A.},
+  journal={Statistical Methods in Medical Research},
+  volume={28},
+  pages={2404--2417},
+  year={2019}
+}
+@article{eletti:2022,
+  title={A unifying framework for flexible excess hazard modelling with applications in cancer epidemiology},
+  author={Eletti, A. and Marra, G. and Quaresma, M. and Radice, R. and Rubio, F.J.},
+  journal={Journal of the Royal Statistical Society: Series C},
+  year={2022},
+  volume={NA},
+  pages={in press}
+}
+@article{rubio:2019b,
+  title={On models for the estimation of the excess mortality hazard in case of insufficiently stratified life tables},
+  author={Rubio, F.J. and Rachet, B. and Giorgi, B. and Maringe, C. and  Belot, A.},
+  journal={Biostatistics},
+  pages={na--na},
+  year={2019}
+}
+@book{kalbfleisch:2011,
+  title={The statistical analysis of failure time data},
+  author={Kalbfleisch, J.D. and Prentice, R.L.},
+  year={2011},
+  publisher={John Wiley \& Sons}
+}
+@article{cox:1972,
+  title={Regression models and life-tables},
+  author={Cox, D.R.},
+  journal={Journal of the Royal Statistical Society: Series B (Methodological)},
+  volume={34},
+  number={2},
+  pages={187--202},
+  year={1972}
+}
+@article{chen:2000,
+  title={Analysis of accelerated hazards models},
+  author={Chen, Y.Q. and Wang, M.C.},
+  journal={Journal of the American Statistical Association},
+  volume={95},
+  number={450},
+  pages={608--618},
+  year={2000}
+}
+@article{chen:2001,
+  title={On a general class of semiparametric hazards regression models},
+  author={Chen, Y.Q. and Jewell, N.P.},
+  journal={Biometrika},
+  volume={88},
+  number={3},
+  pages={687--702},
+  year={2001}
+}
+@article{nikulin:2009,
+  title={On the power generalized {W}eibull family: model for cancer censored data},
+  author={Nikulin, M. and Haghighi, F.},
+  journal={Metron -- International Journal of Statistics},
+  volume={67},
+  number={1},
+  pages={75--86},
+  year={2009}
+}
+@article{stacy:1962,
+  title={A generalization of the gamma distribution},
+  author={Stacy, E.W.},
+  journal={The Annals of Mathematical Statistics},
+  volume={33},
+  number={3},
+  pages={1187--1192},
+  year={1962}
+}
diff --git a/docs/src/index.md b/docs/src/index.md
index b6b2b01..7eafd4d 100644
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -6,9 +6,16 @@ CurrentModule = SurvivalModels
 
 Documentation for [SurvivalModels](https://github.com/JuliaSurv/SurvivalModels.jl).
 
+In this documentation, we can cite stuff from the `references.bib` file like that : [cox1972regression](@cite). 
+
 ```@index
 ```
 
 ```@autodocs
 Modules = [SurvivalModels]
 ```
+
+```@bibliography
+Pages = ["index.md"]
+Canonical = false
+```
\ No newline at end of file
diff --git a/docs/src/references.md b/docs/src/references.md
new file mode 100644
index 0000000..873d4ef
--- /dev/null
+++ b/docs/src/references.md
@@ -0,0 +1,7 @@
+# References
+
+```@bibliography
+```
+ ```@bibliography
+*
+```
\ No newline at end of file
diff --git a/src/NonParametric/KaplanMeier.jl b/src/NonParametric/KaplanMeier.jl
index 4886434..188d9a7 100644
--- a/src/NonParametric/KaplanMeier.jl
+++ b/src/NonParametric/KaplanMeier.jl
@@ -30,8 +30,10 @@ end
 greenwood(S,t) = sum(S.∂σ[i] for i in eachindex(S.t) if S.t[i] < t; init=zero(t))
 
 function StatsAPI.confint(S::KaplanMeier; level::Real=0.05)
-    # TODO 
+    # TODO
 end
-# Other methods to implement for this ? 
-# Maybe a vignette at least ? 
 
+function StatsBase.fit(::KaplanMeier, formula::FormulaTerm, df::DataFrame)
+    resp = modelcols(apply_schema(formula,schema(df)).lhs,df)
+    return KaplanMeier(resp[:,1], resp[:,2])
+end
\ No newline at end of file
diff --git a/src/NonParametric/LogRankTest.jl b/src/NonParametric/LogRankTest.jl
index fff7974..87f14d4 100644
--- a/src/NonParametric/LogRankTest.jl
+++ b/src/NonParametric/LogRankTest.jl
@@ -31,7 +31,7 @@ struct LogRankTest
         # Comput KM numerator and denominators on each strata&group (s,g)
         for s in eachindex(stratas)
             for g in eachindex(groups)
-                idx = (group .== groups[g]) .&& (strata .== stratas[s])
+                idx = Bool.((group .== groups[g]) .* (strata .== stratas[s]))
                 model = KaplanMeier(T[idx], Δ[idx])
                 ∂N[s, g, :], ∂V[s, g, :], D[s, g, :] = model.d, model.d / (model.n - model.d), model.n
             end
diff --git a/src/Parametric/GeneralHazard.jl b/src/Parametric/GeneralHazard.jl
new file mode 100644
index 0000000..be2044b
--- /dev/null
+++ b/src/Parametric/GeneralHazard.jl
@@ -0,0 +1,110 @@
+abstract type AbstractGHMethod end
+struct PHMethod  <: AbstractGHMethod end
+struct AFTMethod <: AbstractGHMethod end
+struct AHMethod  <: AbstractGHMethod end
+struct GHMethod  <: AbstractGHMethod end
+
+c1(::GHMethod,  X1, X2, β, α) = exp.(X2 * α)
+c1(::PHMethod,  X1, X2, β, α) = 1.0
+c1(::AFTMethod, X1, X2, β, α) = exp.(X1 * β)
+c1(::AHMethod,  X1, X2, β, α) = exp.(X2 * α)
+
+c2(::GHMethod,  X1, X2, β, α) = exp.(X1 * β - X2 * α)
+c2(::PHMethod,  X1, X2, β, α) = exp.(-X1 * β)
+c2(::AFTMethod, X1, X2, β, α) = 1.0
+c2(::AHMethod,  X1, X2, β, α) = exp.(-X2 * α)
+
+"""
+    ProportionalHazard(     T, Δ, baseline, X1, X2, optimizer)
+    AcceleratedFaillureTime(T, Δ, baseline, X1, X2, optimizer)
+    AcceleratedHazard(      T, Δ, baseline, X1, X2, optimizer)
+    GeneralHazard(          T, Δ, baseline, X1, X2, optimizer)
+
+Maximum likelihood estimation in General Hazards models using provided `baseline` distribution, provided hazard structure (through the `method` argument), provided design matrices and given optimizer form Optim.jl. 
+
+Parameters `T,Δ` represent observed times and statuses, while `X1, X2` should contain covariates. The number of columns in design matrices can be zero. 
+
+Hazard structures are defined by the method, which should be `<:AbstractGHMethod`, availiable possbilities are `PHMethod()`, `AFTMethod()`, `AHMethod()` and `GHMethod()`.
+
+The baseline distribution should be provided as a `<:Distributions.ContinuousUnivariateDitribution` object from `Distributions.jl` or compliant, e.g. from `SurvivalDistributions.jl`
+
+method: one of NelderMead(), Newton(), LBFGS(), ConjugateGradient() or GradientDescent() or any other method taken by Optim.optimize().
+
+maxit: maximum number of iterations of the optimization routine. 
+
+References: 
+* [Link to my reference so that people understand what it is](https://myref.com)
+"""
+struct GeneralHazardModel{Method, B}
+    T::Vector{Float64}
+    Δ::Vector{Bool}
+    baseline::B
+    X1::Matrix{Float64}
+    X2::Matrix{Float64}
+    α::Vector{Float64}
+    β::Vector{Float64}
+    function GeneralHazardModel(m::Method, T, Δ, baseline, X1, X2, optimizer) where Method<:AbstractGHMethod
+        npd, p, q = length(Distributions.params(baseline())), size(X1,2), size(X2,2)
+        init = zeros(npd+p+q)
+        function mloglik(par::Vector)
+            d, α, β = baseline(exp.(par[1:npd])...), par[npd .+ (1:q)], par[npd + q .+ (1:p)]
+            B = (Method == AHMethod) ? 0.0 : (X1[Δ,:] * β)
+            C = c1(m, X1, X2, β, α)
+            D = c2(m, X1, X2, β, α)
+            return  -sum(loghaz.(d, T[Δ] .* C[Δ]) .+ B) + sum(cumhaz.(d, T .* C) .* D)
+        end
+        par = optimize(mloglik, init, method=optimizer).minimizer
+        d, α, β = baseline(exp.(par[1:npd])...), par[npd .+ (1:q)], par[npd + q .+ (1:p)]
+        return new{Method, typeof(d)}(T, Δ, d, X1, X2, α, β)
+    end
+end
+
+_method(::Type{GeneralHazardModel{M,B}}) where {M,B} = M()
+_baseline(::Type{GeneralHazardModel{M,B}}) where {M,B} = B
+
+const ProportionalHazard{B}      = GeneralHazardModel{PHMethod,  B}
+const AcceleratedFaillureTime{B} = GeneralHazardModel{AFTMethod, B}
+const AcceleratedHazard{B}       = GeneralHazardModel{AHMethod,  B}
+const GeneralHazard{B}           = GeneralHazardModel{GHMethod,  B}
+
+ProportionalHazard(     args...; kwargs...) = GeneralHazardModel(PHMethod(),  args...; kwargs...)
+AcceleratedFaillureTime(args...; kwargs...) = GeneralHazardModel(AFTMethod(), args...; kwargs...)
+AcceleratedHazard(      args...; kwargs...) = GeneralHazardModel(AHMethod(),  args...; kwargs...)
+GeneralHazard(          args...; kwargs...) = GeneralHazardModel(GHMethod(),  args...; kwargs...)
+
+function StatsBase.fit(::Type{GHM}, 
+    formula1::FormulaTerm, 
+    formula2::FormulaTerm, 
+    df::DataFrame, 
+    optimizer) where {GHM <: GeneralHazard}
+    sdf = schema(df)
+    f1_applied, f2_applied = apply_schema.((formula1,formula2),Ref(sdf))
+    X1, X2 = modelcols.((f1,applied.rhs,f2_applied.rhs), Ref(df))
+    TΔ = modelcols(f1_applied.lhs, df)
+    return GeneralHazard(_method(GHM), TΔ[:,1], TΔ[:,2], _baseline(GHM), X1, X2, optimizer)
+end
+# function StatsBase.fit(::Type{GeneralHazard{M,B}}, formula::FormulaTerm, df::DataFrame, optimizer) where {M,B}
+#     sdf = schema(df)
+#     f1_applied = apply_schema(formula,sdf)
+#     X1 = modelcols(f1_applied.rhs,df)
+#     n = size(X1, 1) # number of rows
+#     X2 = Array{Float64, 2}(undef,n,0)
+#     TΔ = modelcols(f1_applied.lhs, df)
+#     return GeneralHazard(M(), TΔ[:,1], TΔ[:,2], B, X1, X2, optimizer)
+# end
+
+"""
+    simGH(n, model::GeneralHazardModel)
+
+This function simulate times to event from a general hazard model, whatever the structure it has (AH, AFT, PH, GH), and whatever its baseline distribution. 
+
+Returns a vector containing the simulated times to event
+
+References: 
+* [HazReg original code](https://github.com/FJRubio67/HazReg) 
+"""
+function simGH(n, m::GeneralHazardModel{M,B}) where {M,B}
+    args = (M(), m.X1, m.X2, m.β, m.α)
+    p0 = 1 .- exp.(log.(1 .- rand(n)) ./ c2(args...))
+    return quantile.(dist,p0) ./ c1(args...)
+end
\ No newline at end of file
diff --git a/src/Semiparametric/CoxMPLE.jl b/src/Semiparametric/CoxMPLE.jl
new file mode 100644
index 0000000..d351906
--- /dev/null
+++ b/src/Semiparametric/CoxMPLE.jl
@@ -0,0 +1,79 @@
+function _cox_nllh(β, t, δ, X)
+    Xβ = X * β # linear predictor.
+    θ = exp.(Xβ)
+
+    # we could just sum them for times that are equals. 
+    # this maping could be done once. 
+
+    prev_i = firstindex(t) # first index of equal times.
+    llh = zero(eltype(β))
+    for i in eachindex(t)
+        # update the first index of equal times: 
+        if t[prev_i] < t[i]
+            prev_i = i
+        end
+        if δ[i]
+            llh += - Xβ[i] + log(sum(@view θ[prev_i:end]))
+        end
+    end
+    return llh
+end
+
+"""
+    CoxModel(init, times, status, des, method, maxit)
+    fit(CoxModel, @formula(Surv(T,Δ)~predictors), dataset, method, maxit)
+
+Maximum Partial likelihood estimation in the semiparametric 
+Cox Proportional Hazards model. W recomand using the formula interface. 
+
+- times : times to event
+- status: vital status indicators (true or 1 = observed, false or 0 = censored)
+- des: design matrix for hazard-level effects
+
+- method: An algorithm from `Optim.jl`, e.g. `NelderMead`, `Newton`, `LBFGS`, `ConjugateGradient` or  `GradientDescent`. For the moment our implementation only supports Optim.jl, but a switch to Optimizations.jl will be done in a second step.
+
+maxit: maximum number of iterations in "method"
+"""
+struct CoxModel
+    par::Vector{Float64}
+    times::Vector{Float64}
+    status::Vector{Bool}
+    des::Matrix{Float64}
+    function CoxModel(init, times, status, des, method, maxit)
+
+        o = sortperm(times)
+        times = times[o]
+        status = Bool.(status[o])
+        des = des[o,:]
+
+        # Let me compute here the llh : 
+        optimiser = optimize(par -> _cox_nllh(par, times, status, des), init, method=method, iterations=maxit)
+        return new(
+            optimiser.minimizer,
+            times,
+            status,
+            des,
+        )
+    end
+end
+
+function StatsBase.fit(::Type{CoxModel},formula::FormulaTerm, df::DataFrame, method, maxit)
+    formula_applied = apply_schema(formula,schema(df))
+    predictors = modelcols(formula_applied.rhs, df)
+    resp = modelcols(formula_applied.lhs, df)
+    return CoxModel(fill(0.0, size(predictors,2)), resp[:,1], resp[:,2], predictors, method, maxit)
+end
+
+# Confidence interval : see in HazReg.
+
+
+# confidence intervals ? 
+# pvalues for netsted models ? 
+# profile likelyhood ? 
+
+
+# we would like to set this up as an optimization routine from Optimization.jl so that is can be solved using different stuff. 
+# we can provide the loss but also the gradient and evne the hessian as formulas are on wikipedia. 
+
+
+
diff --git a/src/SurvivalModels.jl b/src/SurvivalModels.jl
index 686d61d..20b835f 100644
--- a/src/SurvivalModels.jl
+++ b/src/SurvivalModels.jl
@@ -7,9 +7,20 @@ using StatsAPI
 using StatsBase
 using StatsModels
 using DataFrames
+using Distributions
+using Optim
+using LinearAlgebra
+using SpecialFunctions
+using ForwardDiff
+using Random
+using LogExpFunctions
+using SurvivalDistributions
 
 include("NonParametric/KaplanMeier.jl")
 include("NonParametric/LogRankTest.jl")
+include("Semiparametric/CoxMPLE.jl")
+include("Parametric/GeneralHazard.jl")
 
-export KaplanMeier, LogRankTest
-end
+export fit, KaplanMeier, LogRankTest, CoxModel, @formula, Surv, GeneralHazard
+
+end
\ No newline at end of file
diff --git a/test/test.jl b/test/test.jl
new file mode 100644
index 0000000..0455823
--- /dev/null
+++ b/test/test.jl
@@ -0,0 +1,108 @@
+
+@testitem "Check Cox" begin
+
+
+      # Required packages
+      using Distributions, Random, Optim
+      using StableRNGs
+      rng = StableRNG(123)
+
+      # Sample size
+      n = 1000
+
+      # Simulated design matrices
+      dist = Normal()
+      des = hcat(rand(rng, dist, n), rand(rng, dist, n))
+      des_t = rand(rng, dist, n)
+
+      # True parameters
+      theta0 = [0.1,2.0,5.0]
+      alpha0 = 0.5
+      beta0 = [-0.5,0.75]
+
+      # censoring
+      cens = 10
+
+      function qPGW(p, sigma, nu, gamma)
+            val = sigma * ((1 .- log.(1 .- p)) .^ gamma .- 1) .^ (1 / nu)
+            return val
+        end
+
+      function simPH(rng, n, des, theta, beta)
+            #= Uniform variates =#
+            distu = Uniform(0, 1)
+            u = rand(rng, distu, n)
+        
+            #= quantile function =#
+            function quantf(prob)
+                  #= quantile value =#
+                  sigma = theta[1]
+                  nu = theta[2]
+                  gamma = theta[3]
+                  val = qPGW(prob, sigma, nu, gamma)
+                return val
+            end
+            # Linear predictors
+            exp_xalpha = 1.0
+            exp_dif = exp.(-des * beta)
+        
+            # Simulating the times to event
+            p0 = 1 .- exp.(log.(1 .- u) .* exp_dif)
+            times = quantf.(p0) ./ exp_xalpha
+        
+            return times
+        end
+      # Data simulation
+      simdat = simPH(rng, n, des, theta0, beta0)
+
+      # status variable
+      status = collect(Bool,(simdat .< cens))
+
+      # Inducing censoring
+      simdat = min.(simdat, cens)
+
+
+      # Model fit
+
+      OPTCox = CoxModel(
+            fill(0.0, size(des)[2]), 
+            simdat,
+            status,  
+            des,  
+            NelderMead(),
+            1000
+      )
+
+      betahat = OPTCox.par
+      @test betahat[1] ≈ -0.4926892848193542 atol=1e-2
+      @test betahat[2] ≈ 0.6790626074990427 atol=1e-2
+
+      # 95% Confidence intervals under the reparameterisation
+      # CI = ConfInt(FUN = OPTCox[2], MLE = OPTCox[1].minimizer, level = 0.95)
+
+      # CI = DataFrame(CI, :auto)
+      
+      # rename!( CI, ["Lower", "Upper"] )
+
+      # println(CI)
+
+
+end
+
+
+
+@testitem "Check Cox on real data" begin
+
+
+      # Required packages
+      using Distributions, Random, Optim, RDatasets
+
+      ovarian = dataset("survival","ovarian")
+
+      OPTCox = fit(CoxModel, @formula(Surv(FUTime, FUStat) ~ Age + ECOG_PS), ovarian, NelderMead(), 1000)
+
+      betahat = OPTCox.par
+      @test betahat[1] ≈ 0.16149 atol=1e-3
+      @test betahat[2] ≈ 0.0187 atol=1e-3
+
+end
\ No newline at end of file