Skill UI

Toolkit for Python-based survival and time-to-event modeling with scikit-survival, supporting Cox models, ensemble learners, SVMs, and evaluation metrics for censored data.

Build, evaluate, and audit right-censored or competing-risk survival workflows with scikit-survival 0.28.0, covering leakage-safe pipelines, model selection, time-dependent prediction, and censoring-aware metrics such as IPCW concordance, dynamic AUC, and Brier scores.

Perform a priori power analysis to determine required sample sizes for studies. Supports closed-form calculations for t-tests, ANOVA, and regression, plus Monte Carlo simulations for complex models like mixed models and survival analysis. Helps justify sample sizes for grants and protocols by calculating minimum detectable effects and power curves.

Provides comprehensive guidelines for statistical analysis and reporting in biological manuscripts, particularly in cancer research. It teaches researchers how to correctly define 'n' (distinguishing biological from technical replicates), select appropriate statistical tests (ANOVA, survival analysis, etc.), and manage multiple comparisons, ensuring high scientific rigor and reproducibility in published data.

A comprehensive guide for rigorous data analysis execution and reporting in criminology research. This skill covers advanced econometric techniques required for analyzing complex crime data, including count models (Negative Binomial, Zero-Inflated), fixed-effects panel data, and survival/recidivism modeling. It emphasizes transparently reporting uncertainty, addressing selection bias, and ensuring methods are fully reproducible for expert peer review.

This comprehensive guide details the rigorous standards for conducting and reporting advanced demographic analyses. It covers essential techniques, including constructing accurate rates, performing decomposition, generating life tables, and handling event history/survival data. The core focus is on methodological rigor, ensuring the reproducibility of results, and honestly reporting uncertainty (e.g., confidence and credible intervals) to meet the standards of expert peer review.

This guide assists users in structuring and rigorously defending the methodological design for demographic manuscripts. It provides detailed guidance on selecting and justifying advanced techniques—including life tables, decomposition analysis, survival models, Age-Period-Cohort (APC) modeling, and microsimulation. It emphasizes critical steps like establishing identification assumptions, addressing causal biases (selection and exposure), and formulating an adjudication test to rule out rival explanations, ensuring the research is methodologically robust.

A comprehensive guide for conducting advanced, methodologically rigorous data analysis for manuscripts in Entrepreneurship Theory and Practice (ETP). It covers specialized techniques such as survival analysis, handling endogeneity (IV, selection bias), advanced panel data models (Callaway–Sant'Anna), and rigorous qualitative coding schemes. The goal is to ensure that findings are theoretically robust and translate into actionable, venture-relevant insights, moving beyond simple p-value reporting.

A comprehensive guide for applying advanced quantitative methods (OLS, Multilevel, Survival Analysis) to large-scale social surveys (CGSS, CFPS, etc.). This guide emphasizes shifting the focus from purely statistical identification (econometric coefficient races) to deep sociological interpretation, focusing on revealing social mechanisms, structural heterogeneity, and real-world social consequences.