finished everything in journal

Project.toml

@@ -10,6 +10,7 @@ POMDPLinter = "f3bd98c0-eb40-45e2-9eb1-f2763262d755"
 POMDPTools = "7588e00f-9cae-40de-98dc-e0c70c48cdd7"
 POMDPs = "a93abf59-7444-517b-a68a-c42f96afdd7d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+ReadOnlyArrays = "988b38a3-91fc-5605-94a2-ee2116b3bd83"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
@@ -19,6 +20,7 @@ Documenter = "1.8.0"
 POMDPLinter = "0.1"
 POMDPTools = "0.1, 1"
 POMDPs = "0.9, 1"
+ReadOnlyArrays = "0.2.0"
 Statistics = "1"
 StatsBase = "0.32, 0.33, 0.34"
 julia = "1.1"

docs/make.jl

@@ -9,6 +9,11 @@ page_order = [
     "sampling.md",
 ]
 
+PlutoSliderServer.export_directory(
+    "$(@__DIR__)/../notebooks";
+    Export_output_dir="$(@__DIR__)/build/notebooks"
+)
+
 makedocs(
     modules=[ParticleFilters, POMDPs],
     format=Documenter.HTML(),
@@ -17,11 +22,6 @@ makedocs(
     pages = page_order
 )
 
-PlutoSliderServer.export_directory(
-    "$(@__DIR__)/../notebooks";
-    Export_output_dir="$(@__DIR__)/build/notebooks"
-)
-
 deploydocs(
     repo="github.com/JuliaPOMDP/ParticleFilters.jl.git",
     target="build",

docs/src/basic.md

@@ -59,6 +59,8 @@ WeightedParticleBelief()
 
 In order to give access to additional information such as static dynamics parameters, consider using a [`callable object`](https://docs.julialang.org/en/v1/manual/methods/#Function-like-objects). For additional examples, and to re-use elements from the `BootstrapFilter`, see the [`BootstrapFilter`](@ref) source code.
 
+Some building blocks for constructing a `BasicParticleFilter` are provided in the `ParticleFilters` module. For example, the `BasicPredictor`, `BasicReweighter`, `POMDPPredictor`, and `POMDPReweighter` are not exported, but can be used to construct the `predict` and `reweight` functions for a `BasicParticleFilter`. The `check_particle_belief` function can be used as a postprocessing step, and `PostprocessChain` can be used to chain multiple postprocessing steps together. A function `(b, a, o, up.rng) -> ParticleCollection(low_variance_sample(b, 100, up.rng))` or `NormalizedESSConditionalResampler` can be used as a preprocessing step.
+
 ## Reference
 
 ```@docs