fix docs

Author: andreramosfdc

README.md

@@ -71,7 +71,7 @@ prediction = exp.(prediction_log)
 plot(airp.passengers, w=2 , color = "Black", lab = "Historical", legend = :outerbottom)
 plot!(vcat(ones(length(log_air_passengers)).*NaN, prediction), lab = "Forecast", w=2, color = "blue")
 ```
-![quick_example_airp](./docs/assets/quick_example_airp.PNG)
+![quick_example_airp](./docs/src/assets/quick_example_airp.PNG)
 
 ```julia
 N_scenarios = 1000
@@ -84,7 +84,7 @@ end
 plot!(vcat(ones(length(log_air_passengers)).*NaN, exp.(simulation[:, N_scenarios])), lab = "Scenarios Paths", α = 0.1 , color = "red")
 
 ```
-![airp_sim](./docs/assets/airp_sim.svg)
+![airp_sim](./docs/src/assets/airp_sim.svg)
 
 ### Component Extraction
 Quick example on how to perform component extraction in time series utilizing StateSpaceLearning.
@@ -109,7 +109,7 @@ plot(seasonal, w=2 , color = "Black", lab = "Seasonal Component", legend = :oute
 
 ```
 
-| ![quick_example_trend](./docs/assets/trend.svg) | ![quick_example_seas](./docs/assets/seasonal.svg)|
+| ![quick_example_trend](./docs/src/assets/trend.svg) | ![quick_example_seas](./docs/src/assets/seasonal.svg)|
 |:------------------------------:|:-----------------------------:|
 
 
@@ -164,7 +164,7 @@ plot!(real_removed_valued, lab = "Real Removed Values", w=2, color = "red")
 plot!(fitted_completed_missing_values, lab = "Fit in Sample completed values", w=2, color = "blue")
 
 ```
-![quick_example_completion_airp](./docs/assets/quick_example_completion_airp.PNG)
+![quick_example_completion_airp](./docs/src/assets/quick_example_completion_airp.PNG)
 
 ### Outlier Detection
 Quick example of outlier detection for an altered air passengers time-series (artificial NaN values are added to the original time-series).
@@ -188,7 +188,7 @@ plot(log_air_passengers, w=2 , color = "Black", lab = "Historical", legend = :ou
 scatter!([detected_outliers], log_air_passengers[detected_outliers], lab = "Detected Outliers")
 
 ```
-![quick_example_completion_airp](./docs/assets/outlier.svg)
+![quick_example_completion_airp](./docs/src/assets/outlier.svg)
 
 ### StateSpaceModels initialization
 Quick example on how to use StateSpaceLearning to initialize  StateSpaceModels

docs/Project.toml

@@ -1,6 +1,2 @@
 [deps]
-CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
-DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
-GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
-Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"

docs/make.jl

@@ -1,5 +1,6 @@
 using Documenter
-using StateSpaceLearning
+#using StateSpaceLearning
+include("../src/StateSpaceLearning.jl")
 
 # Set up to run docstrings with jldoctest
 DocMeta.setdocmeta!(
@@ -10,12 +11,13 @@ makedocs(;
     modules=[StateSpaceLearning],
     doctest=true,
     clean=true,
+    checkdocs=:none,
     format=Documenter.HTML(mathengine=Documenter.MathJax2()),
     sitename="StateSpaceLearning.jl",
     authors="André Ramos",
     pages=[
         "Home" => "index.md",
-        "manual.md", "adapting_package.md"
+        "adapting_package.md"
     ],
 )
 

docs/assets/airp_sim.svg renamed to docs/src/assets/airp_sim.svg

@@ -16,7 +16,7 @@ StateSpaceLearning.jl is a package for modeling and forecasting time series in a
 
 This package is registered so you can simply `add` it using Julia's `Pkg` manager:
 ```julia
-pkg> add StateSpaceLearning
+pkg.add("StateSpaceLearning")
 ```
 
 ## Citing StateSpaceLearning.jl