for-developers & contributing sections added

Author: shravanngoswamii

tutorials/docs-00-getting-started/Manifest.toml

@@ -0,0 +1,3 @@
+[deps]
+StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
+Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"

tutorials/docs-00-getting-started/Project.toml

@@ -1,14 +1,9 @@
 ---
 title: Getting Started
-permalink: /docs/using-turing/get-started
-redirect_from: docs/0-gettingstarted/
-weave_options:
-  error : false
+engine: julia
 ---
 
-# Getting Started
-
-## Installation
+### Installation
 
 To use Turing, you need to install Julia first and then install Turing.
 
@@ -20,27 +15,28 @@ You will need to install Julia 1.3 or greater, which you can get from [the offic
 
 Turing is an officially registered Julia package, so you can install a stable version of Turing by running the following in the Julia REPL:
 
-```julia
+```{julia}
+#| output: false
 using Pkg
 Pkg.add("Turing")
 ```
 
 You can check if all tests pass by running `Pkg.test("Turing")` (it might take a long time)
 
-## Example
+### Example
 
 Here's a simple example showing Turing in action.
 
 First, we can load the Turing and StatsPlots modules
 
-```julia
+```{julia}
 using Turing
 using StatsPlots
 ```
 
 Then, we define a simple Normal model with unknown mean and variance
 
-```julia
+```{julia}
 @model function gdemo(x, y)
     s² ~ InverseGamma(2, 3)
     m ~ Normal(0, sqrt(s²))
@@ -51,21 +47,19 @@ end
 
 Then we can run a sampler to collect results. In this case, it is a Hamiltonian Monte Carlo sampler
 
-```julia
-chn = sample(gdemo(1.5, 2), NUTS(), 1000)
+```{julia}
+chn = sample(gdemo(1.5, 2), NUTS(), 1000, progress=false)
 ```
 
 We can plot the results
 
-```julia
+```{julia}
 plot(chn)
 ```
 
-In this case, because we use the [normal-inverse gamma distribution](https://en.wikipedia.org/wiki/Normal-inverse-gamma_distribution)
-as a [conjugate prior](https://en.wikipedia.org/wiki/Conjugate_prior), we can compute
-its updated mean as follows:
+In this case, because we use the normal-inverse gamma distribution as a conjugate prior, we can compute its updated mean as follows:
 
-```julia
+```{julia}
 s² = InverseGamma(2, 3)
 m = Normal(0, 1)
 data = [1.5, 2]
@@ -77,7 +71,7 @@ mean_exp = (m.σ * m.μ + N * x_bar) / (m.σ + N)
 
 We can also compute the updated variance
 
-```julia
+```{julia}
 updated_alpha = shape(s²) + (N / 2)
 updated_beta =
     scale(s²) +
@@ -86,11 +80,9 @@ updated_beta =
 variance_exp = updated_beta / (updated_alpha - 1)
 ```
 
-Finally, we can check if these expectations align with our HMC approximations
-from earlier. We can compute samples from a normal-inverse gamma following the
-equations given [here](https://en.wikipedia.org/wiki/Normal-inverse-gamma_distribution#Generating_normal-inverse-gamma_random_variates).
+Finally, we can check if these expectations align with our HMC approximations from earlier. We can compute samples from a normal-inverse gamma following the equations given [here](https://en.wikipedia.org/wiki/Normal-inverse-gamma_distribution#Generating_normal-inverse-gamma_random_variates).
 
-```julia
+```{julia}
 function sample_posterior(alpha, beta, mean, lambda, iterations)
     samples = []
     for i in 1:iterations
@@ -104,7 +96,7 @@ end
 analytical_samples = sample_posterior(updated_alpha, updated_beta, mean_exp, 2, 1000);
 ```
 
-```julia
+```{julia}
 density(analytical_samples; label="Posterior (Analytical)")
 density!(chn[:m]; label="Posterior (HMC)")
 ```

tutorials/docs-00-getting-started/index.qmd

@@ -1,22 +1,19 @@
 ---
 title: Contributing
-permalink: /docs/contributing/guide
-weave_options:
-  error : false
 ---
 
-# Contributing
-
 Turing is an open source project. If you feel that you have some relevant skills and are interested in contributing, then please do get in touch. You can contribute by opening issues on GitHub or implementing things yourself and making a pull request. We would also appreciate example models written using Turing.
 
-Turing has a [style guide](%7B%7Bsite.baseurl%7D%7D/docs/contributing/style-guide). It is not strictly necessary to review it before making a pull request, but you may be asked to change portions of your code to conform with the style guide before it is merged.
+Turing has a [style guide]({{< meta site-url >}}/dev/docs/contributing/style-guide). It is not strictly necessary to review it before making a pull request, but you may be asked to change portions of your code to conform with the style guide before it is merged.
 
 ## How to Contribute
 
 ### Getting Started
 
   - [Fork this repository](https://github.com/TuringLang/Turing.jl#fork-destination-box).
+
   - Clone your fork on your local machine: `git clone https://github.com/your_username/Turing.jl`.
+  
   - Add a remote corresponding to this repository:
 
 `git remote add upstream https://github.com/TuringLang/Turing.jl`.
@@ -27,4 +24,4 @@ Look at the [issues](https://github.com/TuringLang/Turing.jl/issues) page to fin
 
 ### Git Workflow
 
-For more information on how the Git workflow typically functions, please see the [GitHub's introduction](https://guides.github.com/introduction/flow/) or [Julia's contribution guide](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md).
+For more information on how the Git workflow typically functions, please see the [GitHub's introduction](https://guides.github.com/introduction/flow/) or [Julia's contribution guide](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md).

tutorials/docs-01-contributing-guide/index.qmd

@@ -1,12 +1,8 @@
 ---
 title: Style Guide
-permalink: /docs/contributing/style-guide
-weave_options:
-  error : false
+engine: julia
 ---
 
-# Style Guide
-
 Most Turing code follows the [Invenia](https://invenia.ca/labs/)'s style guide. We would like to thank them for allowing us to access and use it. Please don't let not having read it stop you from contributing to Turing! No one will be annoyed if you open a PR with style that doesn't follow these conventions; we will just help you correct it before it gets merged.
 
 These conventions were originally written at Invenia, taking inspiration from a variety of sources including Python's [PEP8](http://legacy.python.org/dev/peps/pep-0008), Julia's [Notes for Contributors](https://github.com/JuliaLang/julia/blob/master/CONTRIBUTING.md), and Julia's [Style Guide](https://docs.julialang.org/en/v1/manual/style-guide/).
@@ -40,7 +36,7 @@ Attempt to follow the [Julia Contribution Guidelines](https://github.com/JuliaLa
 
 If you are a user of Sublime Text we recommend that you have the following options in your Julia syntax specific settings. To modify these settings first open any Julia file (`*.jl`) in Sublime Text. Then navigate to: `Preferences > Settings - More > Syntax Specific - User`
 
-```json
+```{json}
 {
     "translate_tabs_to_spaces": true,
     "tab_size": 4,
@@ -71,7 +67,8 @@ By default, Vim seems to guess that `.jl` files are written in Lisp. To ensure t
 
 Julia provides [test sets](https://docs.julialang.org/en/v1/stdlib/Test/#Working-with-Test-Sets-1) which allows developers to group tests into logical groupings. Test sets can be nested and ideally packages should only have a single "root" test set. It is recommended that the "runtests.jl" file contains the root test set which contains the remainder of the tests:
 
-```julia eval=false
+```{julia}
+#| eval: false
 @testset "PkgExtreme" begin
     include("arithmetic.jl")
     include("utils.jl")
@@ -84,7 +81,8 @@ The file structure of the `test` folder should mirror that of the `src` folder.
 
 Most tests are written in the form `@test x == y`. Since the `==` function doesn't take types into account, tests like the following are valid: `@test 1.0 == 1`. Avoid adding visual noise into test comparisons:
 
-```julia eval=false
+```{julia}
+#| eval: false
 # Yes:
 @test value == 0
 
@@ -96,7 +94,8 @@ In cases where you are checking the numerical validity of a model's parameter es
 
 Here is an example of usage:
 
-```julia eval=false
+```{julia}
+#| eval: false
 # Check that m and s are plus or minus one from 1.5 and 2.2, respectively.
 check_numerical(chain, [:m, :s], [1.5, 2.2]; atol=1.0)
 
@@ -105,4 +104,4 @@ check_gdemo(chain; atol=0.1)
 
 # Checks the estimates for a default MoG model.
 check_MoGtest_default(chain; atol=0.1)
-```
+```