see: An R Package for Visualizing Statistical Models

Abstract

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.

Pavlovian summation: Data and theory.

“…All analyses were performed with R Version 4.0 (2020-04-24; R Core Team, 2020), with packages data.table Version 1.13.0 (Dowle & Srinivasan, 2021), nlstools Version 1.2 (Baty et al, 2015), xfun Version .22 (Xie, 2021), and parameters Version 1.2 (Lüdecke et al, 2020). I used the R function nls to fit Equation 27 to data and the function confint2 from nlstools to calculate 95% confidence intervals (CIs) for g av .…”

A novel coding scheme for assessing responses in divergent thinking: An embodied approach.

“…To do so, we calculated the pairwise phi correlation within each dimension and tested significance with the chi-square test. We used the xtab_statistics() function from the sjstats library in R (Lüdecke, 2018). The largest correlations come from the pairwise pairs of same versus different, concrete versus abstract, and novel versus familiar (see Table 5).…”

Zoos and amphibian conservation: Evaluating the impact of “The Year of The Frog” Campaign

“…Model selection and model averaging, for both random and fixed effects were implemented using a "multimodel" inference approach based on the Akaike's information criterion (Burnham & Anderson, 2002;Burnham et al, 2011) in the "MuMIn" R package, (Bartoń, 2019). Marginal and conditional R 2 was calculated using the "performance" R package (Lüdecke et al, 2020). Model performance was assessed using the "performance" R package using indices of model quality and goodness of fit to select the best models (Lüdecke et al, 2020).…”

“…Marginal and conditional R 2 was calculated using the "performance" R package (Lüdecke et al, 2020). Model performance was assessed using the "performance" R package using indices of model quality and goodness of fit to select the best models (Lüdecke et al, 2020).…”