Name | psycho |
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Stable | |

Documentation | |

Blog | |

Examples | |

Questions | |

Authors | |

Reference |

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!**

- You need some help? You found a bug? You would like to request a new feature? Just open an issue :relaxed:
- Want to add a feature? Correct a bug? Youâ€™re more than welcome to contribute!
- Looking for help to implement the
`analyze`

method for`t.test`

,`cor.test`

and`anova`

.

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
```

The `psycho`

package can already do the following:

- [x] Standardize your data
- [x] Enlight you on how many factors to retain for a PCA
- [x] Give you some clinically relevant info on a participantâ€™s score
- [x] Implements methods for single-case analyses
- [x] Compute complex correlation matrices
- [x] Compute signal detection theory indices (dâ€™, beta, â€¦)
- [x] Help you in the interpretation of various models (mixed, Bayesian, â€¦)

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()`

.

- 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")`

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 ;)