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このリポジトリの skills
SkillsMP は DAAF-Contribution-Community/daaf から 56 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
DAAF-Contribution-Community/daaf収集済み skill 56 件中 16 件を表示しています。
Plotly interactive visualization. Express and Graph Objects: scatter, line, bar, heatmap, 3D, geographic charts; subplots; styling; export. Use when interactivity (hover/zoom) is needed. For static figures use plotnine; for GIS use geopandas. R equivalent:…
原文の言語: 英語
plotnine static visualization (ggplot2 syntax for Python). Geoms, aesthetics, scales, coordinates, facets, themes. Use for static publication-quality figures with grammar-of-graphics syntax. For interactive charts use plotly; for maps use geopandas. R…
原文の言語: 英語
High-performance data manipulation with lazy/eager execution, expressions, I/O (CSV, Parquet, JSON), aggregations, joins, string/datetime ops, and pandas interop. Covers performance optimization patterns and common anti-patterns. DAAF's default DataFrame…
原文の言語: 英語
R statistical modeling with base stats, sandwich, lmtest, car, broom. OLS/WLS/ GLS via lm(), GLMs via glm() (logit, probit, Poisson, negative binomial), robust/clustered SEs via sandwich+lmtest, diagnostics via car, tidy output via broom. Use when execution…
原文の言語: 英語
Machine learning: clustering, PCA/t-SNE/UMAP, classification, prediction regression (Ridge/Lasso/ensemble), cross-validation, Pipelines. For unsupervised analysis, classification, or prediction. For econometric regression use pyfixest/statsmodels. R…
原文の言語: 英語
R machine learning with tidymodels: recipes (preprocessing), parsnip (model specs), workflows (fit pipelines), tune (hyperparameters), rsample (resampling). Engines: ranger (RF), glmnet (lasso/ridge), xgboost, kknn. Model interpretation (permutation…
原文の言語: 英語
Quarto document system for R: .qmd format with knitr engine, YAML frontmatter, code chunks with execution options, rendering to HTML/PDF. DAAF's R notebook format — Stage 9 literally archives executed R scripts in globally and per-chunk non-evaluating chunks…
原文の言語: 英語
Guide for creating DAAF agent definition files. Covers 12-section template, hook registration, skills-in-frontmatter, integration checklist. Use when adding or revising agents. For SKILL.md files, use skill-authoring instead.
原文の言語: 英語
Complex survey analysis in R with survey (Lumley): svydesign, svymean/svytotal, svyglm, svyby domains, replicate weights (BRR, jackknife, bootstrap). Use when execution language is R. Python equivalent: svy. lm(weights=) is NOT survey analysis.
原文の言語: 英語
Complex survey analysis: strata/PSU/weights, variance estimation (Taylor, BRR, jackknife, bootstrap), survey GLM, domain analysis, calibration, survey data I/O (SAS, SPSS, Stata formats). Polars-native. Use for any complex-sample survey: NHANES, CPS, ACS…
原文の言語: 英語
R network analysis with igraph fronted by tidygraph + ggraph: graph construction from edge-list tibbles (tidyverse round-trip), centrality (degree, betweenness, closeness, eigenvector, PageRank), community detection (Leiden, Louvain, walktrap) with seed…
原文の言語: 英語
Python table formatting with great-tables: publication-quality display tables from Polars/pandas DataFrames. GT() grammar-of-tables object, fmt_*() number formatting, tab_*() structure (header, spanners, stub, source notes), tab_style() and data_color()…
原文の言語: 英語
R panel data models with plm: within (FE), random (RE), between, first difference, Fama-MacBeth. Hausman test, panel unit root tests. Includes estimatr for robust estimation and lme4 for mixed effects. Use when execution language is R. Python equivalent:…
原文の言語: 英語
R spatial data: sf vectors, terra rasters, spdep/spatialreg spatial stats, leaflet interactive maps, ggplot2+geom_sf() choropleths. CRS, spatial joins, geometry ops. Use when execution language is R. Python equivalent: geopandas.
原文の言語: 英語
County Presidential Returns 2000-2024 (MIT MEDSL). Vote shares, party trends, turnout by county_fips (joins census/education data). Requires HARVARD_DATAVERSE_API_KEY set via environment_settings.txt. Critical: naive mode='TOTAL' filtering silently drops…
原文の言語: 英語
Translating technical findings for non-technical audiences. Narrative frameworks (Pyramid Principle, SCQA), plain-language translation, executive summaries, policy briefs, causal language. Use when presenting to stakeholders or reviewing deliverables
原文の言語: 英語