Add autoMALA references in docs

docs/README.md

@@ -5,8 +5,14 @@
 From the root of the Pigeons repo:
 
 ```
-julia
-include("docs/make.jl")
+$ julia
+julia> include("docs/make.jl")
+```
+
+To view the generated website, use LiveServer (but don't add it to the Project file)
+```julia
+using LiveServer
+serve(dir="docs/build")
 ```
 
 If the documentation build hangs (https://github.com/Julia-Tempering/Pigeons.jl/issues/60)
@@ -17,4 +23,4 @@ a workaround is the following:
 
 ## Generating a single page
 
-See `docs/toggle.jl`.
+See `docs/toggle.jl`.

docs/src/index.md

@@ -17,6 +17,7 @@ of the following algorithms:
 - Non-Reversible Parallel Tempering (NRPT), 
     [Syed et al., 2021](https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12464).
 - Variational PT, [Surjanovic et al., 2022](https://arxiv.org/abs/2206.00080). 
+- autoMALA, [Biron-Lattes et al., 2023](https://arxiv.org/abs/2310.16782).
 
 These algorithms achieve state-of-the-art performance for approximation 
 of challenging probability distributions.
@@ -146,4 +147,4 @@ citing our work by referring to [our Pigeons paper](https://arxiv.org/abs/2308.0
 
 **APA**
 
-Surjanovic, N., Biron-Lattes, M., Tiede, P., Syed, S., Campbell, T., & Bouchard-Côté, A. (2023). Pigeons.jl: Distributed sampling from intractable distributions. *arXiv:2308.09769.*
+Surjanovic, N., Biron-Lattes, M., Tiede, P., Syed, S., Campbell, T., & Bouchard-Côté, A. (2023). Pigeons.jl: Distributed sampling from intractable distributions. *arXiv:2308.09769.*

docs/src/input-stan.md

@@ -49,7 +49,7 @@ Notice that we have specified a reference distribution, in this case the same mo
 no observations (hence the prior). This needs to be done with Stan targets because it is 
 not possible to automatically extract a prior from a .stan file. 
 
-For a [`StanLogPotential`](@ref), the [`default_explorer()`](@ref) is [`AutoMALA`](@ref). 
+For a [`StanLogPotential`](@ref), the [`default_explorer()`](@ref) is [`AutoMALA`](@ref)[^1]. 
 
 
 
@@ -106,4 +106,7 @@ samples
 <iframe src="../stan_posterior_densities_and_traces.html" style="height:500px;width:100%;"></iframe>
 ```
 
+[^1]: Biron-Lattes, M., Surjanovic, N., Syed, S., Campbell, T., and Bouchard-Côté, A.
+(2023). autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm. *Accepted 
+for AISTATS 2024*. [arXiv:2310.16782](https://arxiv.org/abs/2310.16782).
 

src/explorers/AutoMALA.jl

@@ -20,6 +20,10 @@ condition the problem.
 In normal circumstance, there should not be a need for tuning,
 however the following optional keyword parameters are available:
 $FIELDS
+
+Reference: Biron-Lattes, M., Surjanovic, N., Syed, S., Campbell, T., and Bouchard-Côté, A.
+(2023). autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm. *Accepted 
+for AISTATS 2024*. [arXiv:2310.16782](https://arxiv.org/abs/2310.16782).
 """
 @kwdef struct AutoMALA{T,TPrec <: Preconditioner}
     """