Extracting, Computing and Exploring the Parameters of Statistical Models using R

作者：Daniel Lüdecke, Mattan S. Ben‐Shachar, Indrajeet Patil, Dominique Makowski · 发表于：The Journal of Open Source Software · 年份：2020 · DOI：10.21105/joss.02445 · 被引用次数：549 · 研究领域：Data Analysis with R、Statistical Methods and Inference、Statistical Methods and Bayesian Inference

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...