easystats

More models, more features: what's new in 'parameters' 0.2.0 Sep 30, 2019 The easystats project continues to grow, expanding its capabilities and features, and the parameters package 0.2.0 update is now on CRAN. The primary goal of this package is to provide utilities for processing the parameters of various statistical models. It is useful for end-users as well as developers, as it is a lightweight and open-developed package. The main function, model_parameters(), can be seen as an alternative to broom::tidy(). ... ➦

parameters: a powerful and lightweight alternative to broom to describe your models' coefficients Sep 10, 2019 Okay, “an alternative to broom” might be a bit of an overstatement (at least for now…) But the parameters package, finally on CRAN, already has some cool features! parameters We have recently decided to collaborate around the easystats project, a set of packages designed to make your life easier. This project encompasses several packages, devoted for instance to model internal access, Bayesian analysis, as well as indices of model performance and quality. ... ➦

Check your (Mixed) Model for Multicollinearity with 'performance' Aug 6, 2019 The goal of performance is to provide lightweight tools to assess and check the quality of your model. It includes functions such as r2() for many models (including logistic, mixed and Bayesian models), icc() or helpers to check_convergence(), check_overdipsersion() or check_zero-inflation() (see a complete list of functions here). In this posting, we want to focus on multicollinearity. Multicollinearity “is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others” (source), i. ... ➦

Testing Contrasts from Bayesian Models with 'emmeans' and 'bayestestR' Jun 6, 2019 The Problem with Null Effects Say you fit an ANOVA model, predicting the time it takes to solve a puzzle from its shape (round / square) and whether it was colored or black and white, and you found that one of the estimated effects, in this case the interaction, was not significant. Say even that it was as non-significant as can be, with p = 1.00! options(contrasts = c('contr. ... ➦

Become a Bayesian master with bayestestR (0.2) Jun 3, 2019 bayestestR 0.2 is here! As you might have heard from previous posts, we have recently started to collaborate around the new easystats project, a suite of packages designed to make your life easier. One of the packages, bayestestR, has just been updated on CRAN. And this release is so packed with new features and improvements that it would be impossible to present them all in one post! We have added the computation of different types of Bayes factors (BF), new plotting methods (available in the see package), a new meta-function, describe_posterior, that computes everything at once, and more… ... ➦

The 'see' package: beautiful figures for easystats May 27, 2019 The see package We have recently decided to collaborate around the new easystats project, a set of packages designed to make your life easier. This project encompasses several packages, devoted for instance to model access or Bayesian analysis, indices of model performance or visualisation. Without further ado, please let us introduce the latest addition to the easyverse; the see package! Essentially, see is the visualisation companion to other functions and packages in easystats. ... ➦

A perfectly normally distributed sample: another post? May 13, 2019 Recently, we published a post presenting a small convenience function, from the bayestestR package. This function, named rnorm_perfect, generated an empirical distribution (i.e., a vector of values) as close as possible to a desired distribution, in this case the normal distribution. It worked like this: # Generate a perfect sample x <- rnorm_perfect(n = 100, mean = 0, sd = 1) # Visualise it library(tidyverse) x %>% density() %>% # Compute density function as. ... ➦

Compute R2s and other performance indices for all your models! Apr 30, 2019 Indices of model performance (i.e., model quality, goodness of fit, predictive accuracy etc.) are very important, both for model comparison and model description purposes. However, their computation or extraction for a wide variety of models can be complex. To address this, please let us introduce the performance package! performance We have recently decided to collaborate around the new easystats project, a set of packages designed to make your life easier (currently WIP). ... ➦

How to easily generate a perfectly normal distribution Apr 24, 2019 Many times, for instance when teaching, I needed to quickly and simply generate a perfectly normally distributed sample to illustrate or show some of its characteristics. This is now very easy to do with the new bayestestR package, which includes the rnorm_perfect function. This function is very similar to the classic rnorm (same arguments), with the difference that the generated sample is perfectly normal. Example bayestestR can be installed as follows: ... ➦

Describe and understand Bayesian models and posteriors using bayestestR Apr 15, 2019 The Bayesian framework is quickly gaining popularity among scientists, leading to the growing popularity of packages to fit Bayesian models, such as rstanarm or brms. However, extracting summary indices from these models to report them in your manuscript can be quite challenging, especially for new users. To address this, please let us introduce bayestestR! bayestestR We have recently decided to collaborate around the new easystats project, a set of packages designed to make your life easier (currently very WIP). ... ➦