Joseph Larmarange is a public health demographer and researcher based in Paris with 13 years of experience applying statistical computing to population health questions. As a long-term researcher at IRD, he blends rigorous survey and survival analysis methods with practical tooling, contributing substantive tidyverse-friendly functionality to high-profile R projects like broom, ggally and gtsummary. His open-source work includes harmonizing survival model outputs, adding augment methods for tests, and building survey-aware summary tables and visualization utilities—efforts that make complex epidemiological results more reproducible and presentation-ready. Known for bridging methodological rigor and developer-friendly APIs, he brings both domain expertise in demography and hands-on package development to research software.
Contributions:4 reviews, 62 commits, 26 PRs in 6 years 6 months
Contributions summary:Joseph's commits primarily focus on enhancing the functionality and usability of the ggally R package, which extends ggplot2. They implemented a new function, `ggcoef`, for plotting model coefficients using broom and ggplot2, and also updated existing functions and added tests. The contributions included improvements to existing plotting functions, and the creation of helper functions, demonstrating a focus on improving the package's versatility and the addition of new plot types and functionalities. These additions improve the package's capabilities for data visualization.
Presentation-Ready Data Summary and Analytic Result Tables
Role in this project:
Back-end Developer
Contributions:37 reviews, 47 commits, 17 PRs in 2 years 6 months
Contributions summary:Joseph primarily contributed to the development of the `gtsummary` R package, focusing on enhancing its capabilities related to survey data analysis. Their work involved implementing a `tbl_svysummary()` function to compute descriptive statistics for survey objects, integrating `add_overall()` for summarizing overall statistics, and adding features like `add_p()` for adding p-values for comparisons and `add_ci()` for adding confidence intervals. They also modified the existing code to allow for custom summaries with `tbl_custom_summary()` and ensured compatibility with the `srvyr` package.
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