powerly.dev

What It Is

powerly is an R package for sample size analysis. Its core goal is to identify an appropriate or optimal sample size given a model specification and an outcome measure of interest. It implements the method from Constantin et al. (2023), positioning it more as an academic statistics and experimental design tool than a general-purpose development platform.

Core Features and Technical Approach

Based on the main documentation, powerly uses a three-step recursive algorithm. First, it computes the target outcome across different sample sizes via Monte Carlo simulation. Second, it applies monotone curve-fitting to interpolate the results. Third, it uses stratified bootstrapping to quantify uncertainty around the fitted curve. It provides functions such as generate_model, powerly, and validate, as well as methods like plot and summary, indicating that the tool can not only run analyses but also support validation, visualization, and summary output.

Language, Ecosystem, and Documentation

powerly is explicitly an R package and provides links to CRAN and GitHub, making it suitable for researchers who already work in the R ecosystem. It is released under the MIT license, so its open-source status is clear. In terms of documentation, the site includes Tutorials, Reference, Publications, and News, and links to the manuscript and preprint, making the methodological background relatively transparent. However, based on the main text alone, it is not possible to determine whether the tutorials are sufficiently extensive or whether the API details are fully comprehensive.

Pricing and Deployability

The main text does not mention any commercial pricing, subscription, or enterprise edition. Given the MIT license and availability on CRAN and GitHub, it can be regarded as a free and open-source tool. It is a local R package rather than a hosted SaaS product; “self-hosting” is not especially relevant here, as users would typically install and run it directly on a local machine, server, or research computing environment.

Pros, Cons, and Best-Fit Users

Its strengths are a clearly defined algorithmic goal, open-source availability, a solid academic basis, and direct support for the specialized need of sample size analysis. Its limitations are a relatively narrow scope and a primary focus on the R ecosystem; there is no visible information about Python support, a Web API, team collaboration features, or enterprise support. It is best suited for researchers in fields such as statistics, psychology, medicine, or the social sciences who need model- and simulation-based sample size planning.

Access from China and Alternatives

Access from mainland China cannot be determined from the main text alone; the stability of CRAN, GitHub, and the documentation site may vary across network environments. If access is limited, users can consider CRAN mirrors, GitHub mirrors, or local package caches. Alternative tools may include R packages such as pwr and simr, as well as G*Power, statsmodels, and other power analysis or sample size estimation solutions.