Merge pull request #85 from CDCgov/81-document

Partial documentation of `EpiAware` and also sets documentation standard

EpiAware/Project.toml

@@ -6,6 +6,7 @@ version = "0.1.0-DEV"
 [deps]
 DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
@@ -17,7 +18,9 @@ SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
 
 [compat]
+DataFramesMeta = "0.14"
 Distributions = "0.25"
+DocStringExtensions = "0.9"
 LinearAlgebra = "1.9"
 LogExpFunctions = "0.3"
 Optim = "1.9"
@@ -27,5 +30,4 @@ Random = "1.9"
 ReverseDiff = "1.15"
 SparseArrays = "1.10"
 Turing = "0.30"
-DataFramesMeta = "0.14"
 julia = "1.9"

EpiAware/src/EpiAware.jl

@@ -1,35 +1,47 @@
 """
-    EpiAware
-
-This module provides functionality for calculating Rt (effective reproduction number) with and without
-    considering renewal processes.
-
-# Dependencies
-
-- Distributions: for working with probability distributions.
-- Turing: for probabilistic programming.
-- LogExpFunctions: for working with logarithmic, logistic and exponential functions.
-- LinearAlgebra: for linear algebra operations.
-- SparseArrays: for working with sparse arrays.
-- Random: for generating random numbers.
-- ReverseDiff: for automatic differentiation.
-- Optim: for optimization algorithms.
-- Zygote: for automatic differentiation.
+    module EpiAware
+
+`EpiAware` provides functionality for fitting epidemiological models to data. It is built on
+     top of the `Turing` probabilistic programming language, and provides a set of utilities
+     for constructing and fitting models to data.
+
+## Core model structure
+
+An epidemiological model in `EpiAware` consists of composable structs with core abstract
+    types. The core types are:
+1. `AbstractModel`: This overarching type is used to abstract `Turing` models and is inheried by the other abstract types we use.
+2. `AbstractEpiModel`: Subtypes of this abstract type represent different models for the
+     spread of an infectious disease. Each model type has a corresponding
+     `make_epi_aware` function that constructs a `Turing` model for fitting the
+         model to data. Implemented concrete subtypes:
+    - `Renewal`: A renewal process model for the spread of an infectious disease.
+    - `ExpGrowthRate`: An exponential growth rate model for the spread of an infectious
+         disease.
+    - `DirectInfections`: A model for the spread of an infectious disease based on modelling
+        direct infections.
+3. `AbstractLatentModel`: Subtypes of this abstract type represent different latent
+     processes that can be used in an epidemiological model. Implemented concrete subtype:
+    - `RandomWalk`: A random walk latent process.
+4. `AbstractObservationModel`: Subtypes of this abstract type represent different
+    observation models that can be used in an epidemiological model.
+    Implemented concrete subtypes:
+    - `DelayObservation`: An observation process that models the delay between the time
+        of infection and the time of observation as a convolution, followed by a negative
+        binomial distributed sample.
+
+## Imports
+
+$(IMPORTS)
+
+## Exports
+
+$(EXPORTS)
 
 """
 module EpiAware
 
-using Distributions,
-      Turing,
-      LogExpFunctions,
-      LinearAlgebra,
-      SparseArrays,
-      Random,
-      ReverseDiff,
-      Optim,
-      Parameters,
-      QuadGK,
-      DataFramesMeta
+using Distributions, Turing, LogExpFunctions, LinearAlgebra, SparseArrays, Random,
+      ReverseDiff, Optim, Parameters, QuadGK, DataFramesMeta, DocStringExtensions
 
 # Exported utilities
 export create_discrete_pmf, spread_draws, scan

EpiAware/test/predictive_checking/toy_model_log_infs_RW.jl

@@ -94,7 +94,6 @@ In this case we use the `DirectInfections` model.
 
 rwp = EpiAware.RandomWalk(Normal(),
     truncated(Normal(0.0, 0.01), 0.0, 0.5))
-obs_mdl = delay_observations_model()
 
 #Define the observation model - no delay model
 time_horizon = 100