JASP

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	Y	Y
Bain	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

• 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.
• Importing SQL databases (since v0.16.4)

1. Audit: Planning, selection and evaluation of statistical audit samples, and methods for data auditing (e.g., Benford’s law).
2. Summary statistics: Bayesian inference from frequentist summary statistics for t-test, regression, and binomial tests.
3. Bain: Bayesian informative hypotheses evaluation[8] for t-tests, ANOVA, ANCOVA, linear regression and structural equation modeling.
4. Network: Network Analysis allows the user to analyze the network structure of variables.
5. 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.
6. Machine Learning: The machine Learning module contains 19 analyses for supervised an unsupervised learning:
   • Regression
     1. Boosting Regression
     2. Decision Tree Regression
     3. K-Nearest Neighbors Regression
     4. Neural Network Regression
     5. Random Forest Regression
     6. Regularized Linear Regression
     7. Support Vector Machine Regression
   • Classification
     1. Boosting Classification
     2. Decision Tree Classification
     3. K-Nearest Neighbors Classification
     4. Neural Network Classification
     5. Linear Discriminant Classification
     6. Random Forest Classification
     7. Support Vector Machine Classification
   • Clustering
     1. Density-Based Clustering
     2. Fuzzy C-Means Clustering
     3. Hierarchical Clustering
     4. Neighborhood-based Clustering (i.e., K-Means Clustering, K-Medians clustering, K-Medoids clustering)
     5. Random Forest Clustering
7. SEM: Structural equation modeling.[9]
8. JAGS module
9. Discover distributions
10. Equivalence testing
11. Cochrane meta-analyses

• Official website
• jasp-desktop on GitHub