「nest」の検索結果

Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **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 R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive statistics & Table 1 (gtsummary, modelsummary::datasummary, tableone), (4) classical diagnostic tests (shapiro/jarque.bera.test/bptest/dwtest/bgtest/vif/adf.test/kpss.test/Hausman), (5) baseline modeling (fixest::feols, ivreg, did::att_gt, eventstudyr, sun_ab, did_imputation, synthdid, rdrobust, MatchIt, WeightIt, grf::causal_forest, DoubleML, mediation), (6) robustness battery (modelsummary stack, clubSandwich CRSE, fwildclusterboot, ri2, robomit Oster, bacondecomp, HonestDiD), (7) further analysis (interactions + marginaleffects, mediation::mediate, gsem via lavaan, dose-response splines, grf CATE), (8) publication-ready tables & figures (modelsummary, kableExtra, gt, stargazer, texreg, flextable to LaTeX/Word/HTML; ggplot2 + ggpubr + cowplot + binsreg + iplot for figures). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `WeightIt` / `gfoRmula` / `tmle` / `ltmle`, Mendelian randomization via `MendelianRandomization` / `TwoSampleMR` / `MRPRESSO`, KM / Cox / AFT / RMST survival via `survival` / `survminer` / `flexsurv`, E-value sensitivity via `EValue`, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `DoubleML`, S/T/X/R/DR meta-learners via `causalweight` / `grf`, causal forest via `grf::causal_forest`, BART/BCF via `bartCause` / `bcf`, matrix completion via `MCPanel`, CATE distribution + policy tree via `policytree`, off-policy evaluation, conformal causal via `conformalInference` / `cfcausal`, fairness audit via `fairmodels`, DAG learning via `pcalg` / `bnlearn` / LLM-assisted). Use when the user asks for a complete R empirical analysis, wants a tidyverse-style reproducible R script / Quarto workflow, prefers fixest over reghdfe, needs the R counterpart to StatsPAI / 00.1 / 00.2, or names a specific R step in isolation ("feols with cluster", "MatchIt nearest neighbor", "bacondecomp in R", "gtsummary table 1", "modelsummary to Word"). Mode A triggers on "target trial emulation R", "tmle ltmle", "MendelianRandomization", "TwoSampleMR", "MRPRESSO", "survival cox AFT", "STROBE R", "EValue R", "公共健康 R", "流行病学 R". Mode B triggers on "DoubleML R", "grf causal forest", "policytree", "bartCause bcf", "conformal causal R", "fairmodels", "pcalg NOTEARS", "因果机器学习 R".

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Recommend the right trading workflow, skillset, API profile, and setup path from a natural-language goal. Use this as the on-ramp when a user expresses a trading or investing goal and needs to know which skill/workflow to use, where to start, or whether something works without paid API keys — e.g. "where do I start", "which skill should I use", "I want to swing trade only when the market is favorable", "what works without API keys", "どれを使えばいい", "API キー無しで 使えるものは". Routes and explains only; it never executes trades or auto-runs other skills, and it is honest when no workflow has shipped yet.

Role profile — a reviewer who is fact-seeking, bedrock-seeking, honest, and rejecting. Demands that arguments rest on small facts that cannot be wrong (not on stacked assumptions dressed up as foundation), and that deliberately-unclear concepts be quarantined — never leaked into solid layers, never mixed with each other. Catches the failure mode where designs ship after partial grounding: most of the spec researched, the design feels finished, and the unresearched remainder holds the deal-breaker. Invoke with `/pedantic <target>` (e.g. `/pedantic review this design doc`, `/pedantic review the SDK design`, `/pedantic review this PR`). Probes cover logical structure (definitions, assumptions, boundaries, contradictions, unfalsifiability, vague quantifiers, counterexamples), epistemic honesty (researchable vs. discoverable-only-by-doing), bedrock integrity (assumed-bedrock, leaked uncertainty, mixed unclarity), and design pathology (YAGNI, wrong-layer abstraction, unclear responsibility, fragmentation, over-engineering). Use when you want a hard critique of a plan, design doc, technical doc, code change, or PR description — not a copy edit, not a vibe check.

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Roast a pitch or press release with the eye of a veteran PR director. Honest, sharp, constructive — never cruel for its own sake.

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Run a fast, honest competitive scan — the dimension table built from public evidence (sites, docs, pricing pages, changelogs, reviews), the claims-vs-observed discipline, and the so-what synthesis that ends in moves, not a landscape mural. Use when asked what are competitors doing, quick scan of these three rivals, how does our pricing/feature set compare, or prep the competitive slide honestly. Produces the evidence-based comparison table, the marketing-vs-reality flags, the so-what synthesis, and the staleness date.

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Pick the chart the data and the point actually need — the question-to-chart mapping (comparison, trend, composition, distribution, relationship), the honesty rules (axes, baselines, dual-axis traps), and the one-chart-one-point discipline. Use when asked what chart should I use, make this data visual, why does this chart feel misleading, or fix this graph for the deck. Produces the chart verdict with its reasoning, the honesty checklist applied, and the labeling that lets the chart travel without its author.

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Keep citations honest in business documents — every load-bearing claim sourced, links that actually contain the claim, the as-of dates that keep numbers honest, and the internal-vs-external sourcing rules for decks and memos. Use when asked check the sourcing in this deck, add citations to this doc, our slide says 'studies show' — which studies, or set citation norms for the team. Produces the claim-by-claim audit, the fixes (source found, claim softened, or cut), the citation format for the venue, and the team norm card.

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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).

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Produce honest product reviews and buying guides without fabricated first-hand experience, using disclosed evidence tiers: verified specs, owner-experience synthesis at scale, expert triangulation, and hands-on only when true. Use this skill whenever the user wants to write product reviews or buying guides without hands-on access, set up a review methodology, add evidence disclosure to review content, align reviews with Google's reviews system or FTC affiliate disclosure expectations, or decide when Review and Product schema are honest to use. Triggers on product review, buying guide, best-of list, review methodology, evidence basis, hands-on testing, we tested, affiliate review site, review disclosure, methodology block, original research, reviews system. Also triggers when review content claims testing that did not happen, or when an affiliate site needs a trust mechanism that survives scrutiny.

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Designing side-by-side comparison tools (plan-compare, product-compare, alternative-compare) that help users decide rather than just listing features. Axis selection, default-comparison logic, recommendation discipline. Honest about feature-list-dump (every feature in a row, no decision support), hidden-recommendation (biased comparison pretending to be neutral), and honest-comparison-with-guidance (genuine comparison plus opinionated recommendation) patterns. Triggers on comparison tool, plan compare, product compare, alternative compare, vs page, decision support tool. Also triggers when conversion through comparison stages is poor, when users are abandoning at the comparison step, or when a comparison tool is being scoped for the first time.

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Creates NestJS modules, controllers, services, DTOs, guards, and interceptors for enterprise-grade TypeScript backend applications with dependency injection.

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Flatten deeply nested conditionals into readable, early-return code. Use on any function with 3+ levels of indentation.

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Design NestJS module boundaries and provider ownership. Use only when structuring feature/core/shared modules, controller-service boundaries, or provider lifetime; defer JWT/security, caching, and unrelated request-pipeline recipes.

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Implement distributed cron jobs with Redis-based locking and BullMQ offloading in NestJS. Use when adding @Cron scheduled tasks, preventing duplicate runs across pods, or delegating heavy work to queue workers.

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Implement JWT authentication, RBAC guards, Helmet hardening, and Argon2 hashing in NestJS. Use when adding auth strategies, role-based access control, CSRF protection, or security headers.

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Environment variables validation and ConfigModule setup. Use when validating environment variables with Joi/Zod or configuring ConfigModule in NestJS.

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Build dual-write notification services with database persistence and FCM push delivery in NestJS. Use when creating notification entities, sending push via FCM, or implementing in-app notification feeds.

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Optimize NestJS throughput with Fastify adapter, singleton scope enforcement, compression, and query projections. Use when switching to Fastify, diagnosing request-scoped bottlenecks, or profiling API overhead.

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Implement WebSocket gateways with Socket.io and Server-Sent Events endpoints in NestJS. Use when building chat features, live feeds, or choosing between WebSocket and SSE for real-time communication.

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Create standardized API response envelopes, paginated endpoints, and error interceptors in NestJS. Use when implementing response wrappers, pagination DTOs, or global error formats.

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Implement BullMQ job workflows in NestJS. Use when building queue processors, redis-throttler, Upstash limits, idle polling, stalled jobs, and retention policies.

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Configure structured logging with Pino, Prometheus metrics, and health checks for NestJS services. Use when adding JSON logging, request tracing with correlation IDs, Prometheus metric endpoints, or liveness/readiness health checks.

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Provides AWS Lambda integration patterns for TypeScript with cold start optimization. Use when creating or deploying TypeScript Lambda functions, choosing between NestJS framework and raw TypeScript approaches, optimizing cold starts, configuring API Gateway or ALB integration, or implementing serverless TypeScript applications. Triggers include "create lambda typescript", "deploy typescript lambda", "nestjs lambda aws", "raw typescript lambda", "aws lambda typescript performance".

日本語の概要は準備中です。原文の説明を表示しています。