PSpower 2.0.0

This is a major revision with a fully rewritten codebase, new theoretical foundations, and substantially expanded functionality. The API is not backward-compatible with version 0.1.1.

New features

• power_ps() — sample size and power for the PS-weighted Hájek estimator with continuous or binary outcomes. Supports four estimands (ATE, ATT, ATC, ATO) via closed-form (ATE) or numerical integration (ATT, ATC, ATO, custom tilting functions). Accounts for the confounder coefficient ρ² and the Bhattacharyya overlap coefficient φ.

• power_cox() — sample size and power for the PS-weighted partial likelihood estimator in a Cox proportional hazards model with time-to-event outcomes. Supports randomized trials (robust sandwich variance or Schoenfeld formula) and observational studies (ATE via IPW; ATO and ATT via Monte Carlo design-effect adjustment).

• overlap_coef() — estimates the Bhattacharyya overlap coefficient φ from fitted propensity scores and a treatment indicator, or analytically from Beta distribution parameters.

• S3 print(), summary(), and plot() methods for both power_ps and power_cox result objects. Scalar inputs produce a formatted single-scenario summary; vector inputs produce a multi-scenario grid with a five-number distribution summary and a ggplot2-based sensitivity plot.

Theoretical basis

• Continuous/binary outcomes: Liu, Yang and Li (2026), Annals of Statistics, forthcoming.
• Survival outcomes: Yang, Liu and Li (2026), arXiv:2605.10088.

Changes from version 0.1.1

• The previous single PSpower() function has been replaced by power_ps() and power_cox(), covering a broader set of estimands and outcome types.
• Survival outcomes (time-to-event) are now supported.
• The overlap coefficient φ replaces the previous parameterization.
• The four WATE estimands (ATE, ATT, ATC, ATO) are all supported for continuous/binary outcomes; ATE, ATT, and ATO for survival.