Advances in documentation and examples.

Author: emmanuellujan

Project.toml

@@ -7,7 +7,6 @@ description = "StreamingSampling is a Julia-based proof-of-concept implementatio
 [deps]
 Determinantal = "2673d5e8-682c-11e9-2dfd-471b09c6c819"
 Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
-Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Ipopt = "b6b21f68-93f8-5de0-b562-5493be1d77c9"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"

README.md

@@ -1,8 +1,6 @@
-# StreamingSampling
+# StreamingSampling.jl
 
-StreamingSampling is a Julia-based proof-of-concept implementation of a streamed variant of maximum-entropy sampling (UPmaxentropy). It is designed to process large datasets stored on disk with minimal impact on RAM. The method first computes first-order inclusion probabilities using a DPP-based heuristic, and then feeds these probabilities into the classical UPmaxentropy algorithm to produce diverse samples.
+Documentation for [StreamingSampling](https://github.com/emmanuellujan/StreamingSampling.jl).
 
-## References
+StreamingSampling is a Julia-based proof-of-concept implementation of a streamed variant of maximum-entropy sampling ([UPmaxentropy](https://www.rdocumentation.org/packages/sampling/versions/2.11/topics/UPmaxentropy)). It is designed to process large datasets stored on disk with minimal impact on RAM. The method begins by computing first-order inclusion probabilities using a [DPP](https://dahtah.github.io/Determinantal.jl/dev/)-based heuristic, and then feeds these probabilities into the classical UPmaxentropy algorithm to produce diverse samples.
 
-1. UPmaxentropy — Original maximum-entropy sampling method ([Link](https://www.rdocumentation.org/packages/sampling/versions/2.11/topics/UPmaxentropy))
-2. DPPs for diversity sampling — general background on determinantal point processes ([Link](https://dahtah.github.io/Determinantal.jl/dev/))

docs/Manifest.toml

@@ -1,3 +1,5 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DocumenterCitations = "daee34ce-89f3-4625-b898-19384cb65244"
+Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 StreamingSampling = "e1325ea1-13b9-452d-9115-4dbfefa12b3b"

docs/Project.toml

@@ -10,7 +10,7 @@ DocMeta.setdocmeta!(
     recursive = true,
 )
 
-ENV["BASE_PATH"]    = joinpath(@__DIR__, "../") 
+ENV["BASE_PATH"] = joinpath(@__DIR__, "../") 
 
 # Citations ####################################################################
 bib = CitationBibliography(joinpath(@__DIR__, "citation.bib"))
@@ -54,7 +54,7 @@ makedocs(
       expandfirst = [],
       draft = false,
       pages = ["Home" => "index.md",
-               "Install and run examples" => "install-and-run-examples.md",
+               "Install and run" => "install-and-run.md",
                "Basic examples" => basic_examples,
                "API" => "api.md"],
       format = Documenter.HTML(;

docs/citation.bib

@@ -0,0 +1,9 @@
+# API Reference
+
+This page provides a list of all documented types and functions and in StreamingSampling.jl.
+
+
+```@autodocs
+Modules = [StreamingSampling]
+Order   = [:type, :function, :constant]
+```

docs/make.jl

@@ -2,13 +2,9 @@
 CurrentModule = StreamingSampling
 ```
 
-# StreamingSampling
+# StreamingSampling.jl
 
 Documentation for [StreamingSampling](https://github.com/emmanuellujan/StreamingSampling.jl).
 
-```@index
-```
+StreamingSampling is a Julia-based proof-of-concept implementation of a streamed variant of maximum-entropy sampling ([UPmaxentropy](https://www.rdocumentation.org/packages/sampling/versions/2.11/topics/UPmaxentropy)). It is designed to process large datasets stored on disk with minimal impact on RAM. The method begins by computing first-order inclusion probabilities using a [DPP](https://dahtah.github.io/Determinantal.jl/dev/)-based heuristic, and then feeds these probabilities into the classical UPmaxentropy algorithm to produce diverse samples.
 
-```@autodocs
-Modules = [StreamingSampling]
-```

docs/src/api.md

@@ -0,0 +1,4 @@
+[deps]
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"