Extracting, Computing and Exploring the Parameters of Statistical Models using R
Daniel Lüdecke, Mattan S. Ben‐Shachar, Indrajeet Patil, Dominique Makowski · The Journal of Open Source Software · 2020
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. Aims of the Packageparameters is an R-package (R Core Team, 2020) that fills this important gap.Its primary goal is to provide utilities for processing the parameters of various statistical models.Beyond computing p-values, standard errors, confidence intervals (CI), Bayesian indices and other measures for a wide variety of models, this package implements features like parameters bootstrapping and engineering (such as variables reduction and/or selection), as well as tools for data reduction like functions to perform cluster, factor or principal component analysis.Another important goal of the parameters package is to facilitate and streamline the process of reporting results of statistical models, which includes the easy and intuitive calculation of standardized estimates in addition to robust standard errors and p-values.parameters therefor offers a simple and unified syntax to process a large variety of (model) objects from many different packages.parameters is part of the easystats ecosystem, a collaborative project created to facilitate the usage of R for statistical analyses.Comparison to other Packages parameters functionality is in part comparable to packages like broom (Robinson, Hayes, & Couch, 2020), finalfit (Harrison, Drake, & Ots, 2020) or stargazer (Hlavac, 2018) (and maybe some more).Yet, there are some notable differences, e.g.:• broom (via glance()), finalfit (via ff_metrics()) and stargazer (via stargazer())report fit indices (such as R2 or AIC) by default, while parameters does not.However, there is a dedicated package in the easystats project for assessing regression model quality and fit indices, performance (Lüdecke et al., 2020b).