An R package for experimental psychologists
Efficient and Publishing-Oriented Workflow for Psychological Science
psycho
| Name | psycho |
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| Stable | |
| Documentation | |
| Blog |  |
| Examples |  |
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⚠️ NOTE: This package is being deprecated in favour of the report package. Please check it out and ask for any missing features.
Goal
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 aims at supporting best practices by providing tools to format the output of statistical methods to directly paste them into a manuscript, ensuring standardization of statistical reporting.
Contribute
psycho is a young package in need of affection. You can easily hop aboard the development of this open-source software and improve psychological science:
- Need some help? Found a bug? Request a new feature? Just open an issue ☺️
- Want to add a feature? Correct a bug? You’re more than welcome to contribute!
Don’t be shy, try to code and submit a pull request (PR). Even if unperfect, we will help you to make a great PR!
All contributors will be very graciously rewarded. Someday.
Examples
Check examples in the following vignettes:
Or blog posts:
- The end of errors in ANOVA reporting
- Variable vs. Participant-wise Standardization
- Formatted Correlation with Effect Size
- Extracting a Reference Grid of your Data for Machine Learning Models Visualization
- Copy/paste t-tests Directly to Manuscripts
- Easy APA Formatted Bayesian Correlation
- Fancy Plot (with Posterior Samples) for Bayesian Regressions
- How Many Factors to Retain in Factor Analysis
- Beautiful and Powerful Correlation Tables
- Format and Interpret Linear Mixed Models
- How to do Repeated Measures ANOVAs
- Standardize (Z-score) a dataframe
- Compute Signal Detection Theory Indices
- Installing R, R Studio and psycho
General Workflow
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: summary(), print(), plot() and values().
Installation
- To get the stable version from CRAN, run the following commands in your R console:
install.packages("psycho")
library("psycho")
- To get the latest development version, run the following:
install.packages("devtools")
library("devtools")
install_github("neuropsychology/psycho.R")
library("psycho")
Credits
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