## [3.7.0] - 2026-08-19

- CRITICAL FIX: 22 output-layer tools (Getis-Ord Gi*, Local Moran,
Local Geary C, Bivariate LISA/Lee's L,
OLS/GLR/SAR/SEM/SDM/GWR/MGWR/ESF Regression, SKATER, Multivariate
Clustering, Similarity Search, and 6 center/dispersion tools) were
silently shipping with zero symbology and zero field metadata -
postProcessAlgorithm's QgsProject.instance().mapLayer() lookup
returned None before the layer was added to the project. Switched to
the documented-correct context.getMapLayer(). Hardened the real-QGIS
runtime matrix test with renderer/alias assertions so this can never
silently regress again.

## [3.6.0] - 2026-08-19

- Four flagship LISA-family tools (Getis-Ord Gi*, Local Moran's I,
Local Geary's C, Bivariate LISA) gain a full HTML report with a new
donut chart matching their map symbology 1:1, plus a KPI row and
analyst guidance. Local Geary's C and Bivariate LISA switch onto the
shared LISA quadrant renderer, and Getis-Ord Gi* onto a new shared
gi_confidence_renderer(), removing the last hand-rolled renderer
duplicates. Also closes a real gap: 4 algorithms were missing from the
real-QGIS runtime matrix test entirely - now covers all 81 algorithms.

## [3.5.0] - 2026-08-19

- Two more output layers gain automatic symbology, closing out the
v3.x initiative: GW Summary Statistics colors by gw_std (sequential
quantile), SHAP Spatial Attribution Map auto-symbolizes its highest
mean-|SHAP| field (diverging, zero-centered). Every multi-feature
output-layer algorithm in the plugin now ships with automatic QGIS
symbology.

## [3.4.0] - 2026-08-19

- Seven more output layers gain automatic symbology: DBSCAN and
HDBSCAN clustering now color by cluster_id with noise points (-1) in a
fixed neutral gray; Gaussian Mixture clustering and Spatial k-Fold CV
Evaluator color by cluster_id/cv_fold; Model Residual Spatial
Autocorrelation Check gains diverging residual coloring; Prediction
Uncertainty Map and Conformal Prediction Interval gain a new
sequential single-hue coloring
(core/symbology.py::sequential_quantile_renderer(), colorblind-safe)
on their uncertainty/interval-width field.

## [3.3.0] - 2026-08-19

- Every classifier in the plugin now colors its output layer by
predicted class: Random Forest, Extra Trees, SVC, all 4 Gradient
Boosting engines (via the shared _gbm_base.py), Neural Network (MLP),
Explainable Boosting Machine (EBM), and TabPFN Classification - 10
tools total. Adds core/symbology.py::categorical_field_renderer(), a
new data-driven qualitative renderer that cycles the same 10-color
palette alg_skater.py's region_id already used, by POSITION in the
sorted unique-value list rather than by integer modulo - so it works
for string class labels, not just integer IDs. This completes
automatic symbology for every classification tool in the plugin.

## [3.2.0] - 2026-08-19

- Seven more regression tools gain diverging residual coloring: all 4
Gradient Boosting engines (scikit-learn/XGBoost/LightGBM/CatBoost, via
the shared _gbm_base.py - one edit covers all four), Neural Network
(MLP), Explainable Boosting Machine (EBM), and TabPFN Regression. Same
core/symbology.py::diverging_residual_renderer() pattern as the
previous release. This completes automatic symbology for every
regression tool in the plugin's Group 06 (Machine Learning).

## [3.1.0] - 2026-08-19

- Six regression tools that previously shipped their output layer with
QGIS's default symbology now get the same diverging residual coloring
the spatial-econometric tools already had: Generalized Linear
Regression (which had no postProcessAlgorithm at all until now - it
also gained the layer-metadata aliasing every other tool already has),
Quantile Regression, Spatial Regime Regression, and Random
Forest/Extra Trees/SVR Regression. Uses the new data-driven
diverging_residual_renderer() from core/symbology.py (v3.0.0), which
computes mean and std dev straight off the built layer via QGIS's own
aggregate functions - no new output field needed.

## [3.0.0] - 2026-08-19

- Starts a new major initiative: automatic QGIS symbology
(classification + coloring) for output layers, mirroring the
inline-chart work's approach. Adds core/symbology.py: shared, tested
helpers for the categorized LISA renderer, diverging std-dev renderer,
a new data-driven diverging-residual renderer, a new sequential
quantile renderer for confidence/uncertainty fields, and a new
qualitative cluster-id renderer with noise-point coloring - extracted
from code Local Moran's I, OLS Regression, and SKATER each hand-rolled
independently, which now call the shared helpers instead. About 30
output layers still ship default symbology; wiring them up is the
major-version body of work ahead.

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