Feature/webr playground

.github/workflows/webr-repo.yaml

@@ -24,6 +24,10 @@ on:
     branches: [main, master]
   pull_request:
     branches: [main, master]
+  # Match the pkgdown workflow's release trigger so publishing a release rebuilds
+  # the wasm binary too, keeping it in sync with the docs that install from it.
+  release:
+    types: [published]
   workflow_dispatch:
 
 name: webr-repo.yaml

NEWS.md

@@ -1,6 +1,6 @@
 # LAGOtrials 1.1.0
 
-* Added a live in-browser demo to the documentation site (`live-demo.html`) that runs the real package client-side with webR (R compiled to WebAssembly), so anyone can try `lago_optimization()` with no installation. A GitHub Actions workflow builds the package to WebAssembly with the rwasm toolchain and publishes it as a small CRAN-like repository alongside the site.
+* Added a live in-browser demo to the documentation site (`live-demo.html`) that runs the real package client-side with webR (R compiled to WebAssembly), so anyone can try `lago_optimization()` with no installation, plus an interactive playground (`playground.html`) where you pick a bundled dataset or upload a CSV, configure the model with sliders and toggles, and see the recommendation drawn with the package's own D3 charts alongside a copy-pasteable R snippet. A GitHub Actions workflow builds the package to WebAssembly with the rwasm toolchain and publishes it as a small CRAN-like repository alongside the site.
 * `lago_report()` now renders an interactive HTML dashboard: the confidence set is a hover-enabled D3 plot (a scatter for two components, a strip for one) with the recommended intervention highlighted, and each intervention component gets interactive total-cost and marginal-cost curves. The report stays a single self-contained offline file (D3 is inlined, no CDN or server) and its API is unchanged; rendering now also uses `jsonlite` (a new Suggests).
 * Added an MCP (Model Context Protocol) server to the Python package (`python -m lago.mcp_server`) that exposes `optimize` and `sensitivity` as tools any MCP-aware AI agent can call, plus a `sensitivity()` function in the Python wrapper.
 * Added `lago_sensitivity()`, which re-runs an optimization across a sweep of one input (an outcome or power goal, or a `"cost_multiplier"` that scales all costs) and reports how the recommended intervention, its cost, and the estimated outcome move, with `print()` and `plot()` methods.

README.md

@@ -18,7 +18,7 @@ The LAGOtrials R package bridges the gap between theoretical advances in Learn-A
 3) estimating the optimal intervention based on data from all stages,
 4) calculating the 95% confidence sets for the recommended interventions and the optimal interventions.
 
-> **Try it in your browser, no installation needed:** the [live demo](https://correspondmerchant.github.io/LAGO-R-Package/live-demo.html) runs the real `LAGOtrials` package client-side with [webR](https://docs.r-wasm.org/webr/latest/) (R compiled to WebAssembly). Edit the example and press Run.
+> **Try it in your browser, no installation needed:** the [live demo](https://correspondmerchant.github.io/LAGO-R-Package/live-demo.html) runs the real `LAGOtrials` package client-side with [webR](https://docs.r-wasm.org/webr/latest/) (R compiled to WebAssembly) — edit the example and press Run. For a guided version, the [playground](https://correspondmerchant.github.io/LAGO-R-Package/playground.html) lets you pick a bundled dataset or upload your own CSV, configure the model with sliders and toggles, and see the recommendation as interactive charts plus a copy-pasteable R snippet.
 
 ## Table of Contents
 1. [How to install the R package](#how-to-install-the-r-package)
@@ -42,7 +42,7 @@ The LAGOtrials R package bridges the gap between theoretical advances in Learn-A
   ```
 - Method 2: Clone this repo into RStudio, you can follow the directions provided [in this video](https://www.youtube.com/watch?v=NInwldFZgwA&t=275s).
 
-Not ready to install? Try the package in your browser on the [live demo page](https://correspondmerchant.github.io/LAGO-R-Package/live-demo.html).
+Not ready to install? Try the package in your browser on the [live demo page](https://correspondmerchant.github.io/LAGO-R-Package/live-demo.html), or design an optimization interactively with the [guided playground](https://correspondmerchant.github.io/LAGO-R-Package/playground.html).
 
 ## The main functions
 The LAGOtrials R package has four user-facing functions `lago_optimization()`, `lago_sensitivity()`, `visualize_cost()`, and `lago_report()`.

_pkgdown.yml

@@ -9,12 +9,15 @@ authors:
 
 navbar:
   structure:
-    left: [reference, articles, news, demo]
+    left: [reference, articles, news, demo, playground]
     right: [search, github]
   components:
     demo:
       text: Live demo
       href: live-demo.html
+    playground:
+      text: Playground
+      href: playground.html
 
 news:
   releases:

pkgdown/assets/live-demo.html

@@ -78,6 +78,7 @@
       <h1>LAGOtrials &mdash; live demo</h1>
       <nav>
         <a href="./index.html">Home</a>
+        <a href="./playground.html">Playground</a>
         <a href="./reference/index.html">Reference</a>
         <a href="https://github.com/correspondMerchant/LAGO-R-Package">GitHub</a>
       </nav>
@@ -92,7 +93,7 @@ <h1>LAGOtrials &mdash; live demo</h1>
       so give it a moment.
     </p>
 
-    <div id="status" class="status"><span class="spinner"></span><span id="status-text">Starting webR&hellip;</span></div>
+    <div id="status" class="status" role="status" aria-live="polite"><span class="spinner"></span><span id="status-text">Starting webR&hellip;</span></div>
 
     <label for="code">R code</label>
     <textarea id="code" spellcheck="false"></textarea>