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 and tools to format the output of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity.
Efficient and Publishing-Oriented Workflow for Psychological Science
Name | psycho |
---|---|
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 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.
psycho
is a young package in need of affection. You can easily hop aboard the developpment of this open-source software and improve psychological science:
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.
Check examples in the following vignettes:
Or blog posts:
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()
.
install.packages("psycho")library("psycho")
install.packages("devtools")library("devtools")install_github("neuropsychology/psycho.R")library("psycho")
You can cite the package as following:
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 ;)
hdi
to HDI
bayes_cor.test
simulate_data_regression
standardize
methodget_R2
methodinterpret_odds
and logistic regression effect size interpretationomega_sq
and interpret_omega_sq
analyze.aov
bayesian_cor
to bayes_cor
for consistencyrefdata
dprime
interpret_RMSEA
analyze.lavaan
and analyze.aov
remove_empty_cols
model_to_priors
analyze.htest
for correlations and t-testsinterpret_R2
reorder_matrix
bayes_adj_R2
for loo-adjusted R2 in stanreg modelsget_std_posteriors
for standardized coefs in Bayesian modelsbayesian_cor
for Bayesian correlation tablesrefdata
for reference grid creationrope
for region of practical equivalenceinterpret_r
for correlation coefficient interpretationbayesian_cor.test
, start to work on implementation of bayesian method for correlationfind_matching_string
for fuzzy string matchinganalyze
for psych::fa objectsmellenbergh.test
, crawford.test
and crawford.test.freq
now return a psychobjectassess
has been refactored to become a wrapper for crawford.test
crawford.test
now computes the Bayesian versioncrawford.test
has been renamed to crawford.test.freq
i_am_cheating
parameter to correlation
to prevent p-hackingpercentile
and percentile_to_z
functionsas.data.frame
method for density objectsrnorm_perfect
functiondraws
parameter in get_predicted.stanreganalyze.stanreg
values
in analyzed modelssubset
parameter in standardize
dprime
print
for n_factors
overlap
(experimental) parameter to analyze.stanreg as a different index of effect existenceoverlap
functionpower_analysis
function.analyze.lm
for lm objects.interpret_bf
for bayes factor interpretationprobs_to_odds
odds_to_probs
keep_iterations
in get_predicted.stanreg
(and demonstration of how to plot them in vignettes)emotion
datasetanalyze.stanreg
interpret_d_posterior
for Bayesian size effect interpretationfind_combinations
functionfind_best_model
functionis.standardized
functionget_contrasts.stanreg
and get_predicted.stanreg
functionscrawford_dissociation.test
function for single-case testsaffective
datasetanalyze.stanreg
codestandardize
for vectorsanalyze.stanreg
mellenbergh.test
analyze.stanreg
crawford.test
dprime
function for signal detection theory indices computationcrawford.test
and mellenbergh.test
function for single-case testsnormalize
has been renamed to standardize
#30print
output to correlation
correlation
#25 #24is.psychobject
function.correlation
#23CONTRIBUTING.md
format_digit
except
parameter to normalize
format_digit
except
parameter to normalize
HDI
: Compute highest density intervalsformat_string
: A tidyverse friendly version of sprintf
styler
correlation
: Plot is now supported by ggcorrplot instead of corrplot. The function behaves consistently (plot(correlation(df)
)correlation
: Fix p values adjustmentanalyze.stanreg
: Removed the mean and sd of the print()
, added the MPEanalyze.stanreg
: Returns features of R2 for stan_lmn_factors
: How many factors to retain for PCA or factor analysis?