We are excited to welcome Emi Tanaka and Nima Hejazi to our team of Associate Editors for rOpenSci Stats Software Peer Review. They join Laura DeCicco, Julia Gustavsen, Jouni Helske, Toby Hocking, Rebecca Killick, Anna Krystalli, Mauro Lepore, Noam Ross, Maëlle Salmon, Emily Riederer, Adam Sparks, Beatriz Milz, Margaret Siple and Jeff Hollister. Since 2015, rOpenSci has operated a thorough and collaborative software peer review system. Our editorial team oversees the entire review process — conducting initial checks, selecting reviewers, and guiding the review until the package is approved for inclusion in rOpenSci’s software suite. Given the wide range and number of packages we receive, having a team of editors with diverse and complementary expertise is essential. Emi brings her experiences in experimental design, mixed-effects models and data visualisation, while Nima contributes his expertise in causal inference, de-biased machine learning, and semi-parametric and computational statistics.

Meet our new editors!

Emi Tanaka

Emi is an academic statistician at the Australian National University (ANU), living in Canberra with a passion for data science and open-source software. She has a PhD in Statistics from the University of Sydney. Her primary interest is to develop impactful methods and tools that can be readily used by practitioners. She enjoys working in a collaborative environment with people from diverse backgrounds, with an aim to enhance our knowledge and understanding of the real world data. She interfaces across multiple disciplines to bridge statistical concepts and findings to a broad range of individuals. To this end, she has developed numerous open-source tools, primarily as R-packages, and resources aimed at making statistical methods accessible to a diverse audience. Her proudest work to date is the edibble R package where it reframes the specification of an experimental design by the so-called “grammar of experimental designs” (words = fundamental components of a comparative experiment, e.g. units, treatments and its relationships, and express design as a “sentence” by stringing together “words” that follow a certain grammatical rule).

Emi on GitHub, Website, rOpenSci.

The rOpenSci team has been doing a wonderful job in promoting open science and setting rigorous standards for software development. I’m impressed by their continued dedication to fostering a culture that values open and reproducible research. The team has done so much, including running the coworking space, rOpenSci Champions program and peer-review of open-source software. I had the pleasure of being a mentor in the rOpenSci Champions program last year, and it’s an honour to contribute further as an editor.

Nima Hejazi

Nima is an academic (bio)statistician at the Harvard Chan School of Public Health in Boston. Originally from California, Nima obtained his PhD in Biostatistics at UC Berkeley, where he explored interests in causal inference, semi-parametric statistics, machine learning, and statistical data science. His statistical science research program, driven by applied science collaborations with biomedical and public health scientists, uses causal inference principles to translate scientific questions into precise, interpretable statistical estimands, and then develops methods that incorporate both flexible estimation strategies (via machine learning) and best-in-class uncertainty quantification (using semi-parametric theory) to reliably recover these from data generated by observational studies or randomized experiments. Nima strongly believes that open-source software and open computing practices play a critical role in ensuring reproducibility, replicability, and transparency in modern applied statistics and statistical data science. He previously co-founded the TLverse, a software ecosystem for targeted machine learning in R, and his work has contributed over fifteen open-source packages (almost exclusively in R) to promote the use and accessibility of state-of-the-art statistical methods for modern data analysis.

Nima on GitHub, Website, rOpenSci.

I’ve been following the rOpenSci project since around 2017, when I first heard about it at UC Berkeley, as my own interests in statistical data science and open-source software for statistics were taking shape. Over the years, rOpenSci has made numerous important contributions to the broader landscape around the R language–from tools to support computing infrastructure and statistical data analysis to the R-universe platform and the Stats Peer Review system. With its core mission of promoting open and reproducible research through reliable and reusable open-source software, rOpenSci fills a critical gap in the statistics and data science communities. I’m honored to be able to serve as an editor, and I look forward to contributing to rOpenSci!

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• About rOpenSci Software Peer Review;
• Browse the online book rOpenSci Packages: Development, Maintenance, and Peer Review;
• Read public software review threads on GitHub

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