fix docs

docs/src/index.md

@@ -8,12 +8,13 @@ Documentation for [SimulatedAnnealingABC](/~https://github.com/Eawag-SIAM/Simulate
 
 This package provides different SimulatedAnnealingABC (SABC)
 algorithms for Approximate Bayesian Computation (ABC) (sometimes also called
-_simulation-based inference_ or _likelihood-free inference_").
+_simulation-based inference_ or _likelihood-free inference_).
 
-> [!NOTE]
-> If you are able to compute the density of your posterior, you should
-most likely not be using this or another ABC package.  A traditional MCMC
-algorithm will be much more efficient.
+!!! note
+
+    If you are able to compute the density of your posterior, you should
+    most likely not be using this or another ABC package!  A traditional MCMC
+    algorithm will be much more efficient.
 
 
 
@@ -25,17 +26,17 @@ todo...
 
 ## API
 
-```@index
-```
-
 ```@docs
 sabc
 ```
 
 ```@docs
- update_population!
+update_population!
 ```
 
+```@docs
+SABCresult
+```
 
 ## Related Julia Packages
 
@@ -45,4 +46,4 @@ todo
 
 Albert, C., Künsch, H.R., Scheidegger, A., 2015. A simulated annealing
 approach to approximate Bayes computations. Statistics and computing
-25, 1217–1232. https://doi.org/10.1007/s11222-014-9507-8
+25, 1217–1232. [https://doi.org/10.1007/s11222-014-9507-8](https://doi.org/10.1007/s11222-014-9507-8)

src/SimulatedAnnealingABC.jl

@@ -42,11 +42,13 @@ mutable struct SABCstate
 end
 
 """
-Holds results
+Holds results from a SABC run with fields:
 - `population`: vector of parameter samples from the approximate posterior
 - `u`: transformed distances
 - `ρ`: distances
 - `state`: state of algorithm
+
+The history of ϵ can be accessed with the field `state.ϵ_history`.
 """
 struct SABCresult{T, S}
     population::Vector{T}
@@ -265,7 +267,6 @@ Modifies `population_state`.
 See docstring for `sabc`
 
 """
-
 function update_population!(population_state::SABCresult, f_dist, prior, args...;
                             n_simulation,
                             v=1.0, β=0.8, δ=0.1,
@@ -433,9 +434,7 @@ sabc(f_dist::Function, prior::Distribution, args...;
 - `v = 1.0`: Tuning parameter for XXX
 - `β = 0.8`: Tuning parameter for XXX
 - `δ = 0.1`: Tuning parameter for XXX
-- `type` = `:single` -> single-ϵ
-         = `:multi`  -> multi-ϵ
-         = `:hybrid` -> hybrid multi-u-single-ϵ
+- `type = :single`: Choose algorithm, either `:single` ,`:multi`, or `:hybrid`
 - `resample`: After how many accepted population updates?
 - `checkpoint_history = 1`: every how many population updates distances and epsilons are stored
 - `show_progressbar::Bool = !is_logging(stderr)`: defaults to `true` for interactive use.