Redistricting analysis in R using the redistverse ecosystem. Use whenever the user is working with redist, redistmetrics, ggredist, geomander, adj, alarmdata, PL94171, censable, easycensus, tinytiger, baf, rict, or redistio. Covers the complete pipeline: Census and spatial data loading, adjacency graph construction, SMC/MCMC simulation, constraints (population balance, county splits, VRA compliance), convergence diagnostics, plan metrics (compactness, partisan fairness, splits), visualization, summary tables, and interactive plan drawing. Invoke whenever the user mentions redistricting, gerrymandering, district plans, simulation ensembles, or any redistverse package by name.
---
name: redistricting-analysis
description: 'Redistricting analysis in R using the redistverse ecosystem. Use whenever the user is working with redist, redistmetrics, ggredist, geomander, adj, alarmdata, PL94171, censable, easycensus, tinytiger, baf, rict, or redistio. Covers the complete pipeline: Census and spatial data loading, adjacency graph construction, SMC/MCMC simulation, constraints (population balance, county splits, VRA compliance), convergence diagnostics, plan metrics (compactness, partisan fairness, splits), visualization, summary tables, and interactive plan drawing. Invoke whenever the user mentions redistricting, gerrymandering, district plans, simulation ensembles, or any redistverse package by name.'
---
# Redistricting Analysis with the redistverse
A comprehensive reference for redistricting analysis in R using the redistverse ecosystem (`library(redistverse)` loads `redist`, `redistmetrics`, `ggredist`, `geomander`, `sf`, and `adj`).
## Standard Analysis Pipeline
1. **Load data** — Download pre-built precinct map + demographics + elections via `alarmdata`, or assemble your own `sf` object and build a `redist_map`. → [12-alarmdata.md](references/12-alarmdata.md)
2. **Prepare adjacency** — Verify and edit the adjacency graph (remove water-body edges, fix topological errors). → [08-adj.md](references/08-adj.md), [09-geomander-adjacency.md](references/09-geomander-adjacency.md)
3. **Set constraints** — Define population tolerance, county-split penalties, VRA hinge constraints, compactness. → [02-redist-constraints.md](references/02-redist-constraints.md)
4. **Simulate** — Run `redist_smc()` (recommended) or MCMC to generate an ensemble of valid plans. → [01-redist-simulation.md](references/01-redist-simulation.md)
5. **Validate** — Check convergence (`summary()`), R-hat, effective sample size, and plan diversity. → [01-redist-simulation.md](references/01-redist-simulation.md)
6. **Score** — Compute compactness, partisan fairness, splits, and other metrics across the ensemble. → [04–06 redistmetrics references](references/04-redistmetrics-compactness.md)
7. **Visualize & compare** — Map plans, plot metric distributions, compare enacted plan to the baseline. → [07-ggredist.md](references/07-ggredist.md)
## Quick Navigation
| Package / Topic | Reference |
|----------------|-----------|
| `redist_map`, `redist_smc`, `redist_mergesplit`, `redist_flip`, `redist_plans`, diagnostics | [01-redist-simulation.md](references/01-redist-simulation.md) |
| `redist_constr`, `add_constr_splits`, `add_constr_grp_hinge`, all soft constraints | [02-redist-constraints.md](references/02-redist-constraints.md) |
| `redist_enumpart`, `redist_shortburst`, `redist_cyclewalk`, `init_particles` | [03-redist-advanced.md](references/03-redist-advanced.md) |
| `redistmetrics` — Polsby-Popper, Reock, spanning tree, `prep_perims`, `comp_*` functions | [04-redistmetrics-compactness.md](references/04-redistmetrics-compactness.md) |
| `redistmetrics` — efficiency gap, mean-median, bias, declination, `part_*` functions | [05-redistmetrics-partisan.md](references/05-redistmetrics-partisan.md) |
| `redistmetrics` — county splits, segregation, competitiveness, incumbents, `plan_parity`, `group_frac` | [06-redistmetrics-other.md](references/06-redistmetrics-other.md) |
| `ggredist` — `geom_district`, party color scales, cartographic palettes, `theme_map` | [07-ggredist.md](references/07-ggredist.md) |
| `adj` — adjacency graph construction, edge operations, coloring, Laplacian | [08-adj.md](references/08-adj.md) |
| `geomander` — adjacency construction, contiguity checks, `seam_rip`, edge editing | [09-geomander-adjacency.md](references/09-geomander-adjacency.md) |
| `geomander` — `geo_match`, `estimate_down/up`, `block2prec`, spatial estimation | [10-geomander-spatial.md](references/10-geomander-spatial.md) |
| `geomander` — downloading VEST, ALARM, DRA, HEDA, `get_lewis` election data | [11-geomander-data.md](references/11-geomander-data.md) |
| `alarmdata` — `alarm_50state_map`, pre-built datasets, caching, `alarm_add_plan` | [12-alarmdata.md](references/12-alarmdata.md) |
| `PL94171` — Census P.L. 94-171 decennial data ingestion | [13-pl94171.md](references/13-pl94171.md) |
| `censable` (data + state IDs), `easycensus` (ACS), `tinytiger` (TIGER shapefiles) | [14-census-utilities.md](references/14-census-utilities.md) |
| `baf` — download official Census Bureau block assignment files | [15-baf.md](references/15-baf.md) |
| `rict` — `gt` summary tables: population, demographics, elections, compactness, splits | [16-rict.md](references/16-rict.md) |
| `redistio` — interactive Shiny plan drawing (`draw()`) and adjacency editor (`adj_editor()`) | [17-redistio.md](references/17-redistio.md) |
## Key Data Structures
**`redist_map`** — an `sf` tibble with one row per precinct. Stores the adjacency graph, population column, number of districts, and population tolerance. Created by `redist_map()` or downloaded via `alarm_50state_map()`. Standard columns include `pop`, `pop_black`, `pop_hisp`, `pop_asian`, `pop_white`, `pop_vap`, `pop_bvap`, `ndv` (Democratic votes), `nrv` (Republican votes), and `geometry`.
**`redist_plans`** — a tibble with one row per district per plan (so `nsims × ndists` rows, plus reference plans). Stores district assignments in a hidden integer matrix; metrics are added as columns via `mutate()`. Access the plan matrix with `get_plans_matrix()`.
**`adj`** — an S3 vector class representing an adjacency list. Each element is a zero-indexed integer vector of neighbors. Stored as the `adj` column in `redist_map`.
## Population Tolerance Guidelines
| Map type | Typical `pop_tol` | Legal basis |
|----------|----------|-------------|
| Congressional | `0.005` (±0.5%) | *Wesberry v. Sanders* |
| State legislative | `0.05` (±10%) | *Reynolds v. Sims* |
| Local | Varies by state | State law |
When Claude will use it
Use whenever the user is working with redist, redistmetrics, ggredist, geomander, adj, alarmdata, PL94171, censable, easycensus, tinytiger, baf, rict, or redistio.
---
name: auto-empirical-research-skills
description: Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows, or an end-to-end run from raw data all the way to a finished Word (.docx) manuscript, without reading the entire repository at once.
---
---
name: Full-empirical-analysis-skill
description: Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".
---
