alterlab-qca

Runs Qualitative Comparative Analysis — crisp-set (csQCA), multi-value (mvQCA), and fuzzy-set (fsQCA) — for small-to-medium-N configurational research: calibrating raw data into set membership, building and refining a truth table, and Boolean minimization into conservative / parsimonious / intermediate solutions with consistency and coverage. Because there is no maintained Python QCA library, it shells out to R's QCA package (calibrate, truthTable, minimize) via Rscript and documents that dependency honestly rather than faking a Python API. Use when the request mentions QCA, fsQCA, csQCA, configurational or set-theoretic analysis, necessary/sufficient conditions, truth tables, or calibration of conditions. For net-effect estimation of a single treatment prefer alterlab-causal-inference; for interpretive analysis prefer alterlab-qualitative-methods. Part of the AlterLab Academic Skills suite.

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Repositorio: AlterLab-IEU/AlterLab-Academic-Skills
Última actividad en el origen: 2 de julio de 2026 a las 13:45
Idioma detectado de SKILL.md: inglés
Estrellas: 62
Forks: 10

Opciones de instalación

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npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-qca

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alterlab-skill-finder

The AlterLab front door and multi-agent launcher — routes a task to the right AlterLab skill(s) when the user invokes the suite without naming one, and for a multi-stage goal (or on the keyword 'alterflow', aliases 'alterresearch' / 'ultralab') it CLARIFIES the goal with a few questions, SELECTS the skills the task needs, and runs a dynamic multi-agent workflow composing them (via alterlab-workflow-orchestration, alterlab-research-pipeline, or alterlab-ssci-orchestrator). Triggers on 'use AlterLab skills', 'which AlterLab skill for X', 'is there an AlterLab skill for…', a multi-stage research goal, 'alterflow …', or any generic AlterLab request where the user does not know skill names. It always asks clarifying questions before executing a multi-step run. Use when someone references AlterLab generically, describes a multi-stage goal, or fires the alterflow keyword; when the user already names a specific skill, defer to that skill directly. Part of the AlterLab Academic Skills suite.

2026-07-02

alterlab-meta-analysis

Runs quantitative meta-analysis — computes effect sizes (Hedges' g / standardized mean difference, log odds/risk ratios) with their variances, pools them under fixed-effect and random-effects models, quantifies heterogeneity (I-squared, tau-squared, Cochran's Q), draws forest and funnel plots, and tests publication bias (Egger's regression, trim-and-fill) — using statsmodels.stats.meta_analysis in Python or the field-standard R metafor via Rscript. It enforces PRISMA reporting and the random- vs fixed-effect decision. Use when pooling effect sizes across studies, running a systematic review's quantitative synthesis, or assessing heterogeneity and publication bias. For finding and screening the literature prefer alterlab-deep-research; for a single study's statistics prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.

2026-07-02

alterlab-missing-data

Handles missing data with principled methods — forces an explicit MCAR / MAR / MNAR mechanism statement, then applies multiple imputation by chained equations (MICE) with Rubin's-rules pooling of estimates and standard errors, or full-information maximum likelihood (FIML) where a likelihood/SEM model applies. Uses statsmodels MICE / MICEData in Python or the field-standard R mice via Rscript, and warns that single (mean/regression) imputation and scikit-learn's IterativeImputer return one completed dataset without Rubin's-rules pooling, so they understate standard errors if used as multiple imputation. Use when a dataset has missing values, when choosing an imputation strategy, or when reporting how missingness was handled. For general modeling on complete data prefer alterlab-statistical-analysis; for latent-variable models with FIML prefer alterlab-sem-psychometrics. Part of the AlterLab Academic Skills suite.

2026-07-02

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name	alterlab-qca
description	Runs Qualitative Comparative Analysis — crisp-set (csQCA), multi-value (mvQCA), and fuzzy-set (fsQCA) — for small-to-medium-N configurational research: calibrating raw data into set membership, building and refining a truth table, and Boolean minimization into conservative / parsimonious / intermediate solutions with consistency and coverage. Because there is no maintained Python QCA library, it shells out to R's QCA package (calibrate, truthTable, minimize) via Rscript and documents that dependency honestly rather than faking a Python API. Use when the request mentions QCA, fsQCA, csQCA, configurational or set-theoretic analysis, necessary/sufficient conditions, truth tables, or calibration of conditions. For net-effect estimation of a single treatment prefer alterlab-causal-inference; for interpretive analysis prefer alterlab-qualitative-methods. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Bash(Rscript:*)
compatibility	Requires R with the QCA package (install.packages('QCA'); optionally SetMethods). NO maintained Python QCA library exists, so this skill shells to R via Rscript — declare R as a dependency. No API key. On the Anthropic API (no runtime install) document the R requirement and degrade gracefully.
metadata	{"skill-author":"AlterLab","version":"1.0.0","depends_on":"alterlab-ssci-design-gate (configurational vs net-effects), alterlab-qualitative-methods; audited by alterlab-ssci-inference-gate"}

QCA — Set-Theoretic, Configurational, Equifinal

Skill type: ANALYSIS MODULE. Qualitative Comparative Analysis (Ragin) treats cases as configurations of conditions and asks which combinations are consistently sufficient (or necessary) for an outcome. It is not net-effects regression: it assumes equifinality (several paths to the same outcome), conjunctural causation (conditions work in combination), and asymmetry (the path to Y is not the mirror of the path to not-Y).

Core Mission

QCA IS ABOUT COMBINATIONS AND SET RELATIONS, NOT AVERAGE MARGINAL EFFECTS. CALIBRATE, THEN MINIMIZE.

When to Use This Skill

"Run a fuzzy-set QCA / fsQCA on my configurational data."
"Which combinations of conditions are sufficient for the outcome?"
"Calibrate these variables into set membership and build a truth table."
"Test necessary and sufficient conditions across my ~20 cases."

Does NOT Trigger

The request is really about…	Route to	Why not this skill
The net effect of one treatment (DiD/IV/RDD/regression)	`alterlab-causal-inference`	Correlational/marginal effects, not set relations.
Whether a configurational design is even appropriate	`alterlab-ssci-design-gate`	Design routing, upstream.
Interpretive/thematic analysis of qualitative data	`alterlab-qualitative-methods`	Not set-theoretic.
Plain descriptive statistics	`alterlab-statistical-analysis`	No calibration/minimization.

The R path (verified — QCA package, no Python equivalent)

There is no maintained Python QCA library (the only PyPI option, scpQCA, is stale and non-standard). The field standard is Adrian Dușa's R QCA package. Shell to it via Rscript. The three verified calls:

library(QCA)

# 1. Calibrate raw columns into fuzzy (or crisp) set membership.
#    thresholds for the direct method = c(exclusion, crossover, inclusion), ascending.
dfA  calibraterawA type   thresholds    



tt  truthTabledf outcome   conditions  
                 incl.cut   n.cut   show.cases   sort.by  



sol_c  minimizett details                         
sol_p  minimizett include   details          
sol_i  minimizett include   dir.exp  ABC details

alterlab-qca

Opciones de instalación

Revisa los archivos de origen

QCA — Set-Theoretic, Configurational, Equifinal

Core Mission

When to Use This Skill

Does NOT Trigger

The R path (verified — QCA package, no Python equivalent)

QCA — Set-Theoretic, Configurational, Equifinal

Core Mission

When to Use This Skill

Does NOT Trigger

The R path (verified — QCA package, no Python equivalent)

The discipline QCA demands

Output Template

References

alterlab-qca

Datos de origen

Opciones de instalación

Revisa los archivos de origen

Más de este repositorio

Más de este repositorio

QCA — Set-Theoretic, Configurational, Equifinal

Core Mission

When to Use This Skill

Does NOT Trigger

The R path (verified — QCA package, no Python equivalent)

QCA — Set-Theoretic, Configurational, Equifinal

Core Mission

When to Use This Skill

Does NOT Trigger

The R path (verified — QCA package, no Python equivalent)

The discipline QCA demands

Output Template

References