Extracting, Computing and Exploring the Parameters of Statistical Models using R
By Daniel Lüdecke, Mattan S. Ben-Shachar, Indrajeet Patil, Dominique Makowski. Journal of Open Source Software, 2020
444 citations recorded by the source corpus.
The recent growth of data science is partly fueled by the ever-growing amount of data and the joint important developments in statistical modeling, with new and powerful models and frameworks becoming accessible to users. Although there exist some generic functions to obtain model summaries and parameters, many package-specific modeling functions do not provide such methods to allow users to access such valuable information.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/a8965cb919b426f94f70eef30012182e5822bc13
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Citations in the corpus
- Reproducible Summary Tables with the gtsummary Package (2021, background): “The tbl_regression() function leverages the huge effort behind the broom, parameters (Lüdecke et al., 2020), and broom.”
- effectsize: Estimation of Effect Size Indices and Standardized Parameters (2020, methodology): “…functions, with full documentation and explanation of the various effect sizes offered, and is also used by developers of other R packages as the back-end for effect size computation, such as parameters (Lüdecke et al., 2020), ggstatsplot (Patil, 2018), gtsummary (Sjoberg et al., 2020) and more.”
- Visualizations with statistical details: The 'ggstatsplot' approach (2021, methodology): “Internally, data cleaning is carried out using the tidyverse (Wickham et al., 2019), while statistical analysis is carried out via the statsExpressions (Patil, 2021) and easysta ts (Ben-Shachar et al., 2020; Lüdecke et al., 2019, 2020, 2021; Makowski et al., 2019, 2020) packages.”