Documentation support and release (#46)

docs/src/FirstExample.md

@@ -67,12 +67,22 @@ plot!(sm, r, t-> ccdf(Weibull(1.2,100),t), label = "Baseline survival", color =
 ```
 ![](fig/sm.png)
 
+
+Now we have to specify the support of our joint model. Currently this has to be done by hand. This is used to control the time-points in which we expect observations and is used for numerical procedures. So taking a support that is too big can lead to instabilities. If you redo the plot above with the time range 0 to 500 you will see that the individual survival curves "reaches" 0 around time 100 and the baseline survival "reaches" 0 before time 400.
+```julia
+JointSurvivalModels.support(dist::JointSurvivalModel) = (0.001, 500)
+```
+
+
 To simulate longitudinal measurements ``y_{ij}`` for individual ``i`` at time ``t_ij`` we assume a multiplicative error ``y_{ij} \sim N(m_i(t_{ij}), \sigma * m_i(t_{ij}) ), \sigma = 0.15``. Simulate ``9`` longitudinal measurements and survival times for each individual:
 ```julia
 σ = 0.15
 t_m = range(1,50,9)
 Y = [[rand(Normal(m(i)(t_m[j]), σ * m(i)(t_m[j]))) for j in 1:9] for i in 1:n]
-T = [rand(jm) for jm in joint_models]
+```
+Simulate joint survival times:
+```julia
+T = rand.(joint_models)
 ```
 Additionally we assume right-censoring at ``50`` and no measurements after an event:
 ```julia

docs/src/index.md

@@ -1,24 +1,42 @@
 # JointSurvivalModels.jl
 
-This package implements joint models in julia. It was designed to support the modeling of joint models with probabilistic programming, for example using the Turing.jl framework. Install it with:
+This package implements joint models in Julia. It was designed to support the modeling of joint models with probabilistic programming, for example using the [Turing.jl](https://github.com/TuringLang/Turing.jl) framework. Install it with:
+
 ```julia
 using Pkg
-# needs public listing
-#Pkg.add("JointSurvivalModels")
+Pkg.add("JointSurvivalModels")
 ```
 
-
 The `JointSurvivalModel` type implements a canonical formulation of joint models. It based on a joint hazard function $h(t) = h_0(t) \exp(\gamma' \cdot L(M(t)))$ with a baseline hazard $h_0$ and a link to joint models $L(M(t))$ and coefficients for the links $\gamma$. For a more detailed explanation see [`JointSurvivalModels`](@ref JointSurvivalModel).
 
 
-### A simple example
 
-The hazard of the exponential distribution $Exp(\alpha)$ is the constant function $x\mapsto \alpha$. For the joint longitudinal model we use a simple cosinus function. The joint hazard is then $h(t) = \alpha \exp(\gamma * \cos(t))$.
+## Example
+
+The hazard of the exponential distribution $\text{Exp}(\alpha)$ is the constant function $x\mapsto \alpha$. For the joint longitudinal model we use a simple cosinus function. The joint hazard is then $h(t) = \alpha \exp(\gamma * \cos(t))$.
 
 ```julia
-constant_alpha(x) = 2
-γ = 0.01
+using JointSurvivalModels
+constant_alpha(x) = 0.2
+γ = 0.5
 jm = JointSurvivalModel(constant_alpha, γ, cos)
 ```
+Plotting the survival function vs the baseline hazard:
+```julia
+using StatsPlots, Distributions
+r = range(0,12,100)
+plot(r, ccdf(Exponential(1/0.2), r), label="Baseline survival")
+plot!(r, ccdf(jm, r), label="Joint Survival")
+```
+
+For a more instructive example take a look at the documentation [First Example](@ref) or the case study found in `example/` in the project folder.
 
-For the numeric calculation for the distribution a default support (0,10'000) is assumed. In particular the first events happen after $0$ and the interval (0,10'000) should contain nearly all of the probability mass of the target distribution. If you have different starting times for events or a time horizon that exceeds 10'000 then you can manually adjust the support, see [support in `HazardBasedDistribution`](@ref HazardBasedDistribution)
+
+
+### Support
+
+For the numeric calculation for the distribution a default support (0.001,10'000) is set. In particular the first events happen after $0$ and the interval (0,10'000) should contain nearly all of the probability mass of the target distribution. If you have different starting times for events or a time horizon that is lower or higher than 10'000 then you should manually adjust the support, see [support in `HazardBasedDistribution`](@ref HazardBasedDistribution). For example:
+
+```julia
+JointSurvivalModels.support(dist::HazardBasedDistribution) = (-100, 100)
+```

src/hazarddistribution.jl

@@ -35,7 +35,7 @@ When sampling with `rand` an ODE is solved over the support.
 # Usage
 You should adjust the support according to your data
 ```julia
-JointSurvivalModels.support(dist:HazardBasedDistribution) = (-100, 1000)
+JointSurvivalModels.support(dist::HazardBasedDistribution) = (-100, 1000)
 ```
 """
 support(dist::HazardBasedDistribution) = (1e-4, 10_000)