JSS Journal of Statistical Software

The self-correcting nature of science has been questioned repeatedly in the decade since Ioan-nidis (2005), “Why Most Published Research Findings Are False”. The title of the paper, if not the entirety of its content, was widely publicized and called into question whether scien-tific research was truly contributing to human knowledge and whether public faith in science was misplaced. The existential crisis that ensued within the scientific community identified a lack of replication and reproducibility as a major problem that needed to be addressed. Implementing Reproducible Research aims to provide researchers with tools that facilitate re-producibility and with practices that might push research towards reproducibility as a widely-held standard. The editors define and distinguish between replicability and reproducibility in the preface. Under their definitions, replication involves implementing experiments and collecting new data for analysis; reproducibility involves “the calculation of quantitative sci-entific results by independent scientists using the original datasets and methods”. By these definitions, then, reproducibility is necessary but not sufficient for replication. Several chapters of the book are aimed at researchers performing computational experiments for which experimental data-gathering is not relevant. In such cases, the hurdles to repro