Philip D. Waggoner

The field of statistical computing is rapidly developing and evolving. Shifting away from the formerly siloed landscape of mathematics, statistics, and computer science, recent advancements in statistical computing are largely characterized by a fusing of these worlds; namely, programming, software development, and applied statistics are merging in new and exciting ways. There are numerous drivers behind this advancement, including open movement (encompassing development, science, and access), the advent of data science as a field, and collaborative problem-solving, as well as practice-altering advances in subfields such as artificial intelligence, machine learning, and Bayesian estimation. In this paper, we trace this shift in how modern statistical computing is performed, and that which has recently emerged from it. This discussion points to a future of boundless potential for the field.

Algorithms play an increasingly important role in public policy decision-making. Despite this consequential role, little effort has been made to evaluate the extent to which people trust algorithms in decision-making, much less the personality characteristics associated with higher levels of trust. Such evaluations inform the widespread adoption and efficacy of algorithms in public policy decision-making. We explore the role of major personality inventories -- need for cognition, need to evaluate, the "Big 5" -- in shaping an individual's trust in public policy algorithms, specifically dealing with criminal justice sentencing. Through an original survey experiment, we find strong correlations between all personality types and general levels of trust in automation, as expected. Further, we uncovered evidence that need for cognition increases the weight given to advice from an algorithm relative to humans, and "agreeableness" decreases the distance between respondents' expectations and advice from a judge, relative to advice from a crowd.

Algorithms play an increasingly important role in public policy decision-making. Despite this consequential role, little effort has been made to evaluate the extent to which people trust algorithms in decision-making, much less the personality characteristics associated with higher levels of trust. Such evaluations inform the widespread adoption and efficacy of algorithms in public policy decision-making. We explore the role of major personality inventories -- need for cognition, need to evaluate, the "Big 5" -- in shaping an individual's trust in public policy algorithms, specifically dealing with criminal justice sentencing. Through an original survey experiment, we find strong correlations between all personality types and general levels of trust in automation, as expected. Further, we uncovered evidence that need for cognition increases the weight given to advice from an algorithm relative to humans, and "agreeableness" decreases the distance between respondents' expectations and advice from a judge, relative to advice from a crowd.

The see package is embedded in the easystats ecosystem, a collection of R packages that operate in synergy to provide a consistent and intuitive syntax when working with statistical models in the R programming language (R Core Team, 2021). Most easystats packages return comprehensive numeric summaries of model parameters and performance. The see package complements these numeric summaries with a host of functions and tools to produce a range of publication-ready visualizations for model parameters, predictions, and performance diagnostics. As a core pillar of easystats, the see package helps users to utilize visualization for more informative, communicable, and well-rounded scientific reporting.

A crucial part of statistical analysis is evaluating a model's quality and fit, or performance. During analysis, especially with regression models, investigating the fit of models to data also often involves selecting the best fitting model amongst many competing models. Upon investigation, fit indices should also be reported both visually and numerically to bring readers in on the investigative effort. While functions to build and produce diagnostic plots or to compute fit statistics exist, these are located across many packages, which results in a lack of a unique and consistent approach to assess the performance of many types of models. The result is a difficult-to-navigate, unorganized ecosystem of individual packages with different syntax, making it onerous for researchers to locate and use fit indices relevant for their unique purposes. The performance package in R fills this gap by offering researchers a suite of intuitive functions with consistent syntax for computing, building, and presenting regression model fit statistics and visualizations.