Added pkgdown

.Rbuildignore

@@ -5,3 +5,8 @@
 
 ^README\.qmd$
 ^README\.Rmd$
+
+^_pkgdown\.yml$
+^docs$
+^pkgdown$
+^\.github$

.github/.gitignore

@@ -0,0 +1 @@
+*.html

.github/workflows/pkgdown.yaml

@@ -0,0 +1,50 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown
+
+permissions: read-all
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/[email protected]
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs

.gitignore

@@ -56,3 +56,4 @@ docs/
 # translation temp files
 po/*~
 inst/doc
+docs

DESCRIPTION

@@ -11,6 +11,7 @@ Maintainer: Josh Cowley <[email protected]>
 Description:
   Bayesian Mixture of Experts (MoE) applied to possibly left-censored
   linear regression.
+URL: https://ncljoshcowley.github.io/bmoe/
 License: MIT + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
@@ -37,3 +38,4 @@ Suggests:
     testthat (>= 3.0.0),
     tidyr,
     quarto
+

README.Rmd

@@ -14,29 +14,58 @@ knitr::opts_chunk$set(
 )
 ```
 
-## Purpose
-
 <!-- badges: start -->
 <!-- badges: end -->
 
+## Purpose
+
 This repository contains an R package with a focus on Mixture of Experts (MoE)
     applied to Bayesian linear and censored regression.
+
+Originally used in a yet to be published thesis, **work is ongoing** to make
+    this publicly accessible package easier to use.
+
+Tasks remaining include
+
+* Adding `pkgdown` then improving documentation (`README`, vignettes, etc.),
+
+* Possibly migrating to Stan to avoid the JAGS 'ones trick',
+
+* Removing unnecessary code and dependencies,
+
+* Submitting to CRAN for wider use.
 
 
 
 ## Installation
 
-You can install the development version of `bmoe` from [GitHub](https://github.com/) with:
+You can install the development version of `bmoe` from
+  [GitHub](https://github.com/nclJoshCowley/bmoe) with:
 
 ```{.r}
-devtools::install_github("nclJoshCowley/bmoe", auth_token = ...)
+remotes::install_github("nclJoshCowley/bmoe")
 ```
 
 
 
 ## Model Description
 
-TODO: Include model description here.
+We present the Mixture of Experts model as a finite mixture model of $K$
+    parametric linear regressions, where the concomitant weighting parameters
+    also depend on some predictors.
+
+$$
+    f(\boldsymbol{y_i} | \boldsymbol{x}_i, \boldsymbol{\omega}, \boldsymbol{\theta})
+        = \sum_{k=1}^K
+            \eta_k(\boldsymbol{x}_i | \boldsymbol{\omega}_k)
+            f_k(\boldsymbol{y_i} | \boldsymbol{x}_i, \boldsymbol{\theta}_k)
+$$
+
+Each components' distribution is currently limited to
+    (conditionally independent) multiple linear regressions where each
+    response variable can potentially be left-censored.
+
+<!-- TODO: Article to explain what this model is in semi-technical detail -->
 
 
 
@@ -98,15 +127,8 @@ For example, `y01 + y02 ~ x01 + x02 + x03 | x03` implies two response
 
 ### Reporting
 
-Analysis reports can be generated from any fitted object and desired filename.
+Analysis reports can be generated from any fitted object and desired file name.
 
 ```{.r}
 bmoe::render_bmoe_fit(example_fit, "report-name")
 ```
-
-
-
-## Future Work
-
-Extend this `README` with more functionality including relabelling, prediction
-and log likelihood.

README.md

@@ -1,26 +1,54 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
-
-## Purpose
-
 <!-- badges: start -->
 <!-- badges: end -->
 
+## Purpose
+
 This repository contains an R package with a focus on Mixture of Experts
 (MoE) applied to Bayesian linear and censored regression.
 
+Originally used in a yet to be published thesis, **work is ongoing** to
+make this publicly accessible package easier to use.
+
+Tasks remaining include
+
+- Adding `pkgdown` then improving documentation (`README`, vignettes,
+  etc.),
+
+- Possibly migrating to Stan to avoid the JAGS ‘ones trick’,
+
+- Removing unnecessary code and dependencies,
+
+- Submitting to CRAN for wider use.
+
 ## Installation
 
 You can install the development version of `bmoe` from
-[GitHub](https://github.com/) with:
+[GitHub](https://github.com/nclJoshCowley/bmoe) with:
 
 ``` r
-devtools::install_github("nclJoshCowley/bmoe", auth_token = ...)
+remotes::install_github("nclJoshCowley/bmoe")
 ```
 
 ## Model Description
 
-TODO: Include model description here.
+We present the Mixture of Experts model as a finite mixture model of $K$
+parametric linear regressions, where the concomitant weighting
+parameters also depend on some predictors.
+
+$$
+    f(\boldsymbol{y_i} | \boldsymbol{x}_i, \boldsymbol{\omega}, \boldsymbol{\theta})
+        = \sum_{k=1}^K
+            \eta_k(\boldsymbol{x}_i | \boldsymbol{\omega}_k)
+            f_k(\boldsymbol{y_i} | \boldsymbol{x}_i, \boldsymbol{\theta}_k)
+$$
+
+Each components’ distribution is currently limited to (conditionally
+independent) multiple linear regressions where each response variable
+can potentially be left-censored.
+
+<!-- TODO: Article to explain what this model is in semi-technical detail -->
 
 ## Worked Example
 
@@ -36,18 +64,18 @@ example_sim <- bmoe::example_simulate_bmoe()
 
 example_sim$data
 #> # A tibble: 180 × 4
-#>       y01    x01    x02     x03
-#>     <dbl>  <dbl>  <dbl>   <dbl>
-#>  1  0.567 -2.59  -0.711  0.784 
-#>  2 -5.22  -0.940 -1.14  -1.74  
-#>  3  5.62   0.573  0.285  0.0480
-#>  4 -0.730 -1.27  -1.84  -0.119 
-#>  5  3.97   0.777  0.120  0.769 
-#>  6  9.59   1.72   1.02  -0.127 
-#>  7 -4.38  -0.567 -0.826  0.186 
-#>  8  3.94   0.210  0.510 -0.508 
-#>  9  1.64   0.220 -0.349  1.64  
-#> 10  2.85   0.509 -0.105  1.36  
+#>       y01     x01     x02     x03
+#>     <dbl>   <dbl>   <dbl>   <dbl>
+#>  1 -4.03  -1.07   -0.0269 -1.03  
+#>  2  1.26  -1.36   -1.34    0.901 
+#>  3 -4.38   1.37   -2.21   -0.277 
+#>  4 -0.490 -0.463   0.0581  0.0647
+#>  5 -0.211  0.0454 -0.446  -0.406 
+#>  6  7.45   0.456   0.956   0.927 
+#>  7  4.92   1.03    1.78    2.56  
+#>  8 -0.140 -0.312  -0.428   0.222 
+#>  9 -0.162  0.195   0.0182 -0.836 
+#> 10  2.69   2.47   -1.46   -0.473 
 #> # ℹ 170 more rows
 ```
 
@@ -110,13 +138,8 @@ based on the linear predictor formed from `x03`.
 ### Reporting
 
 Analysis reports can be generated from any fitted object and desired
-filename.
+file name.
 
 ``` r
 bmoe::render_bmoe_fit(example_fit, "report-name")
 ```
-
-## Future Work
-
-Extend this `README` with more functionality including relabelling,
-prediction and log likelihood.

_pkgdown.yml

@@ -0,0 +1,5 @@
+url: https://ncljoshcowley.github.io/bmoe/
+
+template:
+  bootstrap: 5
+