Refresh the documentation landing page and the roadmap

ROADMAP.md

@@ -1,9 +1,18 @@
 # `microdf` roadmap
 
-`microdf` currently provides capabilities for analyzing weighted microdata, including statistics, distributional tables, graphs, and special functions for working with PSL Tax-Calculator. In the future, it will provide more functionality, including:
-* Charts showing distributional changes between a baseline and reform policy
-* Extending these charts to more than one reform
-* Presets for working with common datasets, e.g. suggesting the appropriate weight for SCF and CPS
-* Standard error calculations for surveys with replicate weight files
+`microdf` provides weighted data structures for survey microdata: weighted
+quantiles and moments, inequality measures including the Gini coefficient and
+top and bottom shares, Foster-Greer-Thorbecke poverty measures, and variance
+estimation from replicate weights.
 
-See the [issues page](https://github.com/PolicyEngine/microdf/issues) to view and suggest other items.
+Planned work:
+
+* Variance from stratum and cluster identifiers, for designs where replicate
+  weights are not published
+* Presets for common datasets, such as suggesting the appropriate weight
+  variable for the SCF and the CPS
+* Wider coverage of pandas methods that change shape, so fewer operations need
+  an explicit override
+
+See the [issues page](https://github.com/PolicyEngine/microdf/issues) to view
+and suggest other items.

changelog.d/docs-landing.changed.md

@@ -0,0 +1 @@
+Adds an installation snippet and links to the API reference from the documentation landing page, and updates the roadmap.

docs/examples.md

@@ -1,7 +1,7 @@
 Examples
 ========
 
-See these rendered Jupyter notebooks for examples of `microdf` usage.
+Worked examples of `microdf` usage. The [Gini coefficient notebook](gini.ipynb) shows the estimator against a known distribution.
 ## Keeping weights through pandas operations
 
 `MicroSeries` and `MicroDataFrame` retain independent copies of their row

docs/home.md

@@ -1,4 +1,22 @@
 `microdf` documentation
 =======================
 
-This includes example notebooks, and in the future will also include function documentation.
+`microdf` provides weighted data structures for survey microdata analysis in
+Python. `MicroSeries` and `MicroDataFrame` carry sampling weights inside the
+object, so the weights stay aligned with their rows through merges, filters,
+grouping and reindexing, and the weighted estimators — quantiles, variance,
+Gini, top shares, poverty rates — use documented conventions rather than ad hoc
+ones.
+
+```python
+import microdf as mdf
+
+df = mdf.MicroDataFrame({"income": [10_000, 30_000, 120_000]}, weights=[800, 1_200, 50])
+df.income.median()  # 30000, weighted
+df.income.gini()
+```
+
+Install with `pip install microdf-python`.
+
+- [Examples](examples.md) — worked analyses, and how weights survive a pipeline
+- [API reference](api.md) — every weighted estimator and weight-handling method