JASP

JASP

Stable release	0.16.1 / February 17, 2022 (2022-02-17)
Repository	JASP Github page
Written in	C++, R, JavaScript
Operating system	Microsoft Windows, Mac OS X and Linux
Type	Statistics
License	GNU Affero General Public License
Website	jasp-stats.org

JASP (Jeffreys’s Amazing Statistics Program^[1]) is a free and open-source program for statistical analysis supported by the University of Amsterdam. It is designed to be easy to use, and familiar to users of SPSS. It offers standard analysis procedures in both their classical and Bayesian form.^[2][3] JASP generally produces APA style results tables and plots to ease publication. It promotes open science by integration with the Open Science Framework and reproducibility by integrating the analysis settings into the results. The development of JASP is financially supported by several universities and research funds.

JASP screenshot

Analyses[]

JASP offers frequentist inference and Bayesian inference on the same statistical models. Frequentist inference uses p-values and confidence intervals to control error rates in the limit of infinite perfect replications. Bayesian inference uses credible intervals and Bayes factors^[4][5] to estimate credible parameter values and model evidence given the available data and prior knowledge.

The following analyses are available in JASP:

Analysis	Frequentist	Bayesian
A/B test		Y
ANOVA, ANCOVA, Repeated measures ANOVA and MANOVA	Y	Y
AUDIT (module)	Y	Y
Bain (module)	Y	Y
Binomial test	Y	Y
Confirmatory factor analysis (CFA)	Y
Contingency tables (including Chi-squared test)	Y	Y
Correlation:[6] Pearson, Spearman, and Kendall	Y	Y
Equivalence T-Tests: Independent, Paired, One-Sample	Y
Exploratory factor analysis (EFA)	Y
Linear regression	Y	Y
Logistic regression	Y
Log-linear regression	Y	Y
Machine Learning	Y
Mann-Whitney U and Wilcoxon	Y	Y
Mediation Analysis	Y
Meta Analysis	Y	Y
Mixed Models	Y	Y
Multinomial test	Y	Y
Network Analysis	Y
Principal component analysis (PCA)	Y
Reliability analyses: α, γδ, and ω	Y	Y
Structural equation modeling (SEM)	Y
Summary Stats[7]		Y
T-tests: independent, paired, one-sample	Y	Y
Visual Modeling: Linear, Mixed, Generalized Linear	Y

Other features[]

• Descriptive statistics.
• Assumption checks for all analyses, including Levene's test, the Shapiro–Wilk test, and Q–Q plot.
• Imports SPSS files and comma-separated files.
• Open Science Framework integration.
• Data filtering: Use either R code or a drag-and-drop GUI to select cases of interest.
• Create columns: Use either R code or a drag-and-drop GUI to create new variables from existing ones.
• Copy tables in LaTeX format.
• Plot editing, Raincloud plots.
• PDF export of results.

Modules[]

1. Summary statistics: Bayesian inference from frequentist summary statistics for t-test, regression, and binomial tests.
2. BAIN: Bayesian informative hypotheses evaluation^[8] for t-test, ANOVA, ANCOVA and linear regression.
3. Network: Network Analysis allows the user to analyze the network structure of variables.
4. Meta Analysis: Includes techniques for fixed and random effects analysis, fixed and mixed effects meta-regression, forest and funnel plots, tests for funnel plot asymmetry, trim-and-fill and fail-safe N analysis.
5. Machine Learning: Machine Learning module contains 13 analyses for supervised an unsupervised learning:
   • Regression
     1. Boosting Regression
     2. K-Nearest Neighbors Regression
     3. Random Forest Regression
     4. Regularized Linear Regression
   • Classification
     1. Boosting Classification
     2. K-Nearest Neighbors Classification
     3. Linear Discriminant Classification
     4. Random Forest Classification
   • Clustering
     1. Density-Based Clustering
     2. Fuzzy C-Means Clustering
     3. Hierarchical Clustering
     4. K-Means Clustering
6. SEM: Structural equation modeling.^[9]
7. JAGS module
8. Discover distributions
9. Equivalence testing
10. Cochrane meta-analyese

References[]

External links[]

• Official website
• jasp-desktop on GitHub