High-performance Stata plugins for kernel-based statistical methods and decision trees, written in C. Includes standalone utility commands.
| Plugin | Description | Key Features |
|---|---|---|
| kdensity2 | Kernel density estimation | 1D/MV, target split (train/predict), multi-group, product kernel, CV bandwidth. GPU acceleration via make kdensity2_cuda (hidden feature). |
| nwreg | Nadaraya-Watson / local polynomial kernel regression | 1D/MV, target split (train/predict), multi-group, CV bandwidth, robust SE, local polynomial (poly()), derivatives (derivatives()). GPU acceleration via make nwreg_cuda (hidden feature). |
| fangorn | CART decision tree / random forest | Gini/Entropy/MSE, pre-sorted splits, CV depth selection, OOB error, MDI importance, mtry, ntiles quantile strategy, Mermaid export |
| xpofangorn | Partially linear model via DML | Double machine learning, K-fold cross-fitting, auto-detect binary/continuous treatment, robust and cluster SE, all fangorn options pass-through |
Note: 对于因果推断，更好的方法是使用 causal forest 和 generalized random forest（Athey & Imbens, 2016; Athey, Tibshirani & Wager, 2019），仍待开发。当前
xpofangorn通过双重机器学习（DML）提供了部分线性模型的估计，而fangorn的target()选项提供了一种简化的反事实预测方式，但两者尚不具备异质性处理效应（CATE）的无偏估计、honest 分裂等 causal forest 的核心特性。
| Command | Description |
|---|---|
| csadensity | Common support area between treatment and control groups (kernel-based) |
| bprecall | Binary classification metrics (precision, recall, accuracy, F1) |
| countdistinct | Count distinct value combinations across variables |
| dta2md | Export .dta metadata & descriptive statistics to Markdown (LLM-readable dataset documentation) |
| gen_init_var | Initialize panel variable by carrying forward a base-year value |
| gencatutility | Compute continuous utility scores for categorical variables |
| labelvalidsample | Create binary marker for complete-case observations |
All estimation plugins (kdensity2, nwreg, fangorn) support a target split via the target(varname) option:
- target=0 = training set — these observations contribute to bandwidth selection / model training
- target=1 = target/prediction set — these observations receive predictions but do NOT influence training
This is particularly useful for treatment/control analysis: train on the control group (target=0), then predict the counterfactual density or regression outcome for the treatment group (target=1). Both groups receive estimates, but bandwidths and model parameters are determined solely by the training set.
An advantage over official Stata commands: kdensity2 and nwreg handle multi-dimensional grouping natively (2+ group variables). Official kdensity only supports a single by() group variable and cannot use string group variables directly. In this toolkit, string group variables are auto-encoded to numeric via egen group() in the ado layer.
HHStataToolkit/
├── src/ # Shared C infrastructure
│ ├── stplugin.h/c # Stata plugin interface (official, do not modify)
│ └── utils.h/c # Kernels, bandwidth, Stata↔C I/O, memory helpers
├── Makefile # Multi-plugin build system
├── kdensity2/ # Kernel density plugin (single-file C)
├── nwreg/ # Nadaraya-Watson regression plugin (single-file C)
├── fangorn/ # Decision tree / random forest (multi-file C)
├── xpofangorn/ # DML partially linear model (pure Stata, calls fangorn)
├── single_ado/ # Pure Stata commands (no compilation needed)
├── test/ # Test do-files, organised per plugin
│ ├── kdensity2/
│ ├── nwreg/
│ ├── fangorn/
│ │ ├── benchmark/ # Unified DT + RF benchmark vs scikit-learn
│ │ ├── test_fangorn_basic.do # Quick integration smoke test
│ ├── xpofangorn/ # DML partially linear model tests
│ │ ├── test_fangorn_cv.do # CV depth selection test
│ │ ├── test_fangorn_phase1.do # Phase 1 decision tree tests
│ │ ├── test_fangorn_phase2.do # Phase 2 random forest tests
│ │ ├── test_fangorn_regularization.do # Regularization tests
│ │ └── test_mermaid_output.do # Mermaid export tests
│ └── csa/ # csadensity tests
└── AGENTS.md # Agent instruction file (replaces CLI help for AI)
# Build all plugins
make
# Build individual plugins
make kdensity2
make nwreg
make fangorn
# Install to ~/ado/plus/ (both plugins + single_ado)
make install
# Package for distribution
make dist
# Reproducibility tests (bit-identical 10-run)
stata -b do test/kdensity2/test_seed_reproducibility.do
stata -b do test/kdensity2/test_cpu_reproducibility.do
stata -b do test/nwreg/test_seed_reproducibility.do
stata -b do test/nwreg/test_cpu_reproducibility.do
stata -b do test/fangorn/test_fangorn_seed_reproducibility.do
# Functional tests
stata -b do test/kdensity2/test_chi2_group.do
stata -b do test/nwreg/test_nwreg_simulation.do
stata -b do test/nwreg/test_local_polynomial.do
stata -b do test/nwreg/test_local_polynomial_reproducibility.do
stata -b do test/fangorn/test_fangorn_phase1.do
stata -b do test/fangorn/test_fangorn_phase2.do
stata -b do test/fangorn/test_fangorn_regularization.do
stata -b do test/fangorn/test_fangorn_basic.do
stata -b do test/fangorn/test_fangorn_cv.do
stata -b do test/csa/test_csadensity.doThis project was developed with AI-assisted tooling:
- Orchestration: OpenCode + Oh-My-OpenAgent
- Models: kimi-for-coding (frontend/reasoning) + DeepSeek V4 Flash (backend/execution)
- Linux (64-bit, GCC)
- macOS (Intel & Apple Silicon, Clang + brew install libomp)
- Windows (64-bit, MinGW cross-compile)
MIT. stplugin.h and stplugin.c are official StataCorp files distributed
under their own terms.