The main goal of the
psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, to facilitate and speed up the time spent on data analysis. It implements various useful functions with a special focus on the output, which becomes something readable that can be, almost directly, copied and pasted into a report or a manuscript.
Want to get involved in the developpment of an open-source software and improve psychological science? Join us!
Check examples in the following vignettes: - Overview of the psycho package - Bayesian Analysis in Psychology
Or run the following:
library(rstanarm) library(psycho) df <- psycho::affective # Load a dataset from the psycho package df <- standardize(df) # Standardize all numeric variables fit <- stan_glm(Age ~ Salary, data=df) # Fit a Bayesian linear model results <- analyze(fit) # Format the output print(results) summary(results) plot(results) contrasts <- get_contrasts(results, "Salary") # Compute estimated means and contrasts contrasts$means contrasts$contrasts get_predicted(fit) # Get model prediction
psycho package can already do the following:
The package revolves around the
psychobject. Main functions from the package return this type, and the
analyze() function transforms other R objects into psychobjects. Four functions can then be applied on a psychobject:
To get the latest development version, run the following:
install.packages("devtools") library("devtools") install_github("neuropsychology/psycho.R") library("psycho")
You can cite the package as following: - Makowski, (2018). The psycho Package: an Efficient and Publishing-Oriented Workflow for Psychological Science. Journal of Open Source Software, 3(22), 470. https://doi.org/10.21105/joss.00470
Please remember that
psycho is a high-level package that heavily relies on many other packages, such as tidyverse, psych, qgraph, rstanarm, lme4 and others (See Description for the full list of dependencies). Please cite their authors ;)