The fect R package (fixed effects counterfactual estimators) needed simultaneous work on six interdependent fronts: structural refactoring, CV unification, C++ convergence conditioning, plot overhaul, a 12-chapter Quarto user manual, and bug fixes.

Prompt:

work on https://github.com/statsclaw/example-fect resolve the issues,
and document any related files to my workspace

Over 5 days and ~20 workflow runs, with ~10 substantive researcher interactions guiding the process, StatsClaw helped:

• Grow the test suite from 131 to 590 tests (100% pass rate)
• Make 33+ commits on the cfe branch
• Catch 11+ bugs through adversarial BLOCK signals — several would have been hard to find manually
• Surface a balanced-panel algebraic degeneracy through behavioral testing
• Improve C++ EM convergence accuracy through component-wise monitoring
• Produce a 12-chapter Quarto book with DGPs and companion scripts

The researcher's domain decisions (e.g., "use interaction(region, time)", "reduce tau to 1") were essential — the system could not have made these calls on its own.

• Adversarial BLOCK signals as discovery: The balanced-panel degeneracy was found purely by the Tester's behavioral test — neither the Planner nor Builder anticipated it
• Sequential validation: Phase 1 verified before Phase 2 starts, preventing error compounding across interdependent changes
• Cross-session continuity: 5 days of coherent development thanks to automatic HANDOFF.md
• Book-derived tests: Quarto chapter examples became test cases (+98 tests)
• Mathematical comprehension: The convergence fix required understanding the IFE decomposition Y = mu11' + alpha1' + 1xi' + FLambda' + epsilon

Build a Python equivalent of the interflex R package for conditional marginal effect estimation, starting from zero Python code. No formal math document provided; the R source is the only specification. Required 3 rounds of researcher feedback to get right.

Prompt:

Read https://github.com/xuyiqing/interflex This is one R package.
Now build a Python package from it. Only translate the estimator = "linear"
part and all related functions into the new Python package. Ship everything
to statsclaw/example-R2PY and related documents into my workspace.

In 3 iterative rounds with ~150 words of user input:

Round 1 — Initial Construction:

• Planner reverse-engineered all contracts from R source (no math doc existed)
• Builder implemented 14-module dispatcher architecture
• Tester built 34 independent validation tests

Round 2 — API Refinement:

• User requested import interflex; interflex(data, ...) (callable module pattern)
• Required DML as a mandatory dependency using Python DoubleML
• Builder implemented a types.ModuleType subclass to make the module callable

Round 3 — Quality Audit:

• Found 6 bugs in code that passed all 34 tests
• 2 bugs would have produced silently wrong statistical results:
  • Bootstrap inference silently fell through to delta method (elif vartype == "bootstrap": pass)
  • HC1 sandwich operator precedence (NumPy * binds before @)

Final product: 14 modules, ~3,500 lines, 34 tests, 10-chapter Quarto book. The audit step was critical — without it, 2 silent bugs would have shipped.

• Specification recovery: Planner extracted implicit contracts from R source — what each function guarantees, what invariants hold, what edge cases exist
• Cross-language translation: Handled R/Python idiom differences (formula interface, factor handling, freq_weights routing)
• Bug discovery beyond testing: 6 bugs in passing code caught by Reviewer's cross-pipeline audit
• Iterative specification emergence: The full spec emerged through 3 rounds of dialogue, not upfront documentation

Implement three probit estimation methods from a 4-page PDF specification in C++ via Rcpp/Armadillo, then run a comprehensive Monte Carlo study comparing all three.

Prompt:

Build the R works from this PDF. Three probit estimation methods in C++ via
Rcpp/Armadillo: MLE (Newton-Raphson), Bayesian Gibbs sampler (Albert-Chib
data augmentation), and random-walk Metropolis-Hastings. After building, run
a Monte Carlo simulation comparing all three on bias, RMSE, coverage, and
computational speed across N={200,500,1000,5000} with 500 replications per
scenario. Show all results. Target repo: statsclaw/example-probit. Ship it.
Save the documents in my workspace.

StatsClaw activated Workflow 11 (Simulation Study) — the full three-pipeline architecture:

1. Planner ingested the PDF, comprehended all three estimation methods (Newton-Raphson MLE, Albert-Chib Gibbs with truncated normals, random-walk MH with proposal tuning), and produced three independent specifications

2. Builder (from spec.md only) implemented three C++ functions using Armadillo, with R wrappers providing a consistent API

3. Tester (from test-spec.md only) validated MLE against glm(family=binomial(link="probit")) at 10^-6 tolerance, tested Bayesian posterior convergence, verified edge cases

4. Simulator (from sim-spec.md only) ran 6,000 total simulations (4 sample sizes x 500 reps x 3 methods), producing comparison tables for bias, RMSE, coverage, and speed

The Simulator treated the estimator as a black box — calling the implementation without knowing how it was built, providing a third independent verification pathway.

• Paper-to-package pipeline: PDF equations → working C++ code through the comprehension protocol
• Three-pipeline architecture: Code, test, and simulation pipelines all isolated — a bug must fool all three
• C++ integration: Rcpp/Armadillo compilation, export attributes, numerical stability (overflow in Phi, Mills ratio near tails)
• Monte Carlo as verification: Simulation results independently confirm theoretical properties (unbiasedness, coverage, efficiency)
• Reduced manual effort: Much of the boilerplate (Rcpp setup, simulation harness, documentation) was handled by the agents

Add a type = "network" visualization to the panelView R package: build a bipartite graph from the panel's observation matrix (units × time periods), highlight singletons (degree-1 nodes), draw convex hulls around connected components, and support k-partite graphs for multi-way FE. Based on Figure 2 of Correia (2016).

Prompt:

Read this paper: Correia (2016), "A Feasible Estimator for Linear Models
with Multi-Way Fixed Effects". I want panelView to make figures like
Figure 2 and identify degree-1 nodes (singletons). Add type = "network":
build a bipartite graph from the panel's observation matrix. Units and
time periods as differently shaped/colored nodes, edges = "unit observed
in period". Highlight singletons, draw convex hulls around connected
components. Support 2+ sets of fixed effects (k-partite). igraph in
Suggests, not Imports. Plan first.

The Planner comprehended Correia (2016) and identified that the existing codebase — a monolithic 37KB file — needed refactoring before a new feature could be safely added. StatsClaw made the judgment to refactor first:

1. Refactored monolith → 4 focused modules (plot-treat.R, plot-outcome.R, plot-bivariate.R, + dispatch core)
2. Added test suite covering all plot types, sub-modes, both formula and explicit-variable interfaces
3. Fixed 3 ggplot2 deprecation bugs (size → linewidth, unsafe class() checks)
4. Replaced stale vignette with 5-chapter Quarto manual
5. Added ARCHITECTURE.md (13.9 KB) documenting dispatch architecture and 8-stage data pipeline
6. CRAN prep: zero errors, zero warnings in R CMD check, version bumped to v1.2.1
7. Produced specs for the type = "network" feature as a clean new module

• Paper comprehension → feature design: The Planner read a methodology paper not to implement the estimator, but to extract a visualization concept from its exposition
• Proactive refactor-first judgment: The system identified refactoring as a prerequisite before the user asked for it
• Test coverage from zero: Added tests for a package that previously had none
• CRAN compliance: Handled warnings, DESCRIPTION fields, stale vignettes systematically