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Welcome to Psycho's for Julia documentation.

Note

The package is not released yet. Help for its development is very much appreciated.

## Installation

``pkg> add https://github.com/neuropsychology/Psycho.jl.git``

## Goal

`Psycho`'s primary goal is to fill the gap between Julia's output and the formatted result description of your manuscript, with the automated use of best practices guidelines, ensuring standardization and quality of results reporting. It also provides useful tools and functions for psychologists, neuropsychologists and neuroscientists for their everyday data analyses.

## Quick Example

``````using GLM, Psycho

# Simulate some data
data = simulate_data_correlation([[0.3], [0.1]])

# Standardize the results
standardize!(data)

# Describe the data
report(data)``````
``````The data contains 200 observations of the following variables:
- y (Mean = 0 ± 1.0 [-2.92, 3.07])
- Var1 (Mean = 0 ± 1.0 [-2.35, 3.25])
- Group (1FD, 50.0%; 2HA, 50.0%)``````
``````using GLM

# Fit a Linear Model
model = lm(@formula(y ~ Var1 * Group), data)

# Report the results
results = report(model)``````
``````We fitted a linear regression to predict y with Var1 and Group (Formula: y ~ 1 + Var1 + Group + Var1 & Group). The model's explanatory power (R²) is of 0.05 (adj. R² = 0.04). The model's intercept is at 0.0. Within this model:
- Var1 is significant (Coef = 0.3, t(196) = 3.05, 95% CI [0.11; 0.49], p < .01) and can be considered as small (Std. Coef = 0.3).
- Group: 2HA is not significant (Coef = 0, t(196) = 0, 95% CI [-0.27; 0.27], p > .1) and can be considered as very small (Std. Coef = 0).
- Var1 & Group: 2HA is not significant (Coef = -0.2, t(196) = -1.44, 95% CI [-0.47; 0.07], p > .1) and can be considered as very small (Std. Coef = -0.2).``````