Cleanup

.gitignore

@@ -6,6 +6,7 @@
 /dev/
 /docs/build/
 /docs/src/generated/
+/docs/src/examples/
 /docs/site/
 */.ipynb_checkpoints
 .vscode/settings.json

README.md

@@ -5,9 +5,9 @@
 [![CI](https://github.com/JuliaGaussianProcesses/EasyGPs.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/JuliaGaussianProcesses/EasyGPs.jl/actions/workflows/CI.yml)
 [![Codecov](https://codecov.io/gh/JuliaGaussianProcesses/EasyGPs.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/JuliaGaussianProcesses/EasyGPs.jl)
 
-EasyGPs.jl is a package that defines a high-level API for the JuliaGaussianProcesses eco-
-system. It is aimed at people who want to use GPs to do exploratory analysis, model data and
-make predictions without having to deal with all the low-level detail.
+EasyGPs.jl is a package that defines a high-level API for the JuliaGaussianProcesses
+ecosystem. It is aimed at people who want to use GPs to do exploratory analysis, model data
+and make predictions without having to deal with all the low-level detail.
 
 > [!NOTE]  
 > This is currently an experimental package and may undergo rapid breaking changes.

docs/src/index.md

@@ -1,8 +1,8 @@
 # EasyGPs.jl
 
-EasyGPs.jl is a package that defines a high-level API for the JuliaGaussianProcesses eco-
-system. It is aimed at people who want to use GPs to do exploratory analysis, model data and
-make predictions without having to deal with all the low-level detail.
+EasyGPs.jl is a package that defines a high-level API for the JuliaGaussianProcesses
+ecosystem. It is aimed at people who want to use GPs to do exploratory analysis, model data
+and make predictions without having to deal with all the low-level detail.
 
 !!! note
     This is currently an experimental package and may undergo rapid changes.

examples/0-mauna-loa/script.jl

@@ -1,8 +1,8 @@
 # # Mauna Loa time series example
 #
 # In this notebook, we apply Gaussian process regression to the Mauna Loa CO₂
-# dataset. It is adapted from the corresponding AbstractGPs.jl tutorial:
-# https://juliagaussianprocesses.github.io/AbstractGPs.jl/stable/examples/1-mauna-loa/
+# dataset. It is adapted from [the corresponding AbstractGPs.jl tutorial](
+# https://juliagaussianprocesses.github.io/AbstractGPs.jl/stable/examples/1-mauna-loa/)
 # It is therefore instructive to read that first and then see how EasyGPs.jl
 # simplifies the steps.
 
@@ -16,13 +16,6 @@ using Plots  # visualisation
 
 # Let's load and visualize the dataset.
 
-# !!! tip
-#     The `let` block [creates a new
-#     scope](https://docs.julialang.org/en/v1/manual/variables-and-scoping/#scope-of-variables),
-#     so any utility variables we define in here won't leak outside. This is
-#     particularly helpful to keep notebooks tidy! The return value of the
-#     block is given by its last expression.
-
 (xtrain, ytrain), (xtest, ytest) = let
     data = CSV.read(joinpath(@__DIR__, "CO2_data.csv"), Tables.matrix; header=0)
     year = data[:, 1]