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{{cite journal | vauthors = Quintana DS, Williams DR | title = Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP | language = En | journal = BMC Psychiatry | volume = 18 | issue = 1 | pages = 178 | date = June 2018 | pmid = 29879931 | pmc = 5991426 | doi = 10.1186/s12888-018-1761-4 | doi-access = free }}{{cite journal | vauthors = Brydges CR, Gaeta L | title = An Introduction to Calculating Bayes Factors in JASP for Speech, Language, and Hearing Research | language = En | journal = Journal of Speech, Language, and Hearing Research | volume = 62 | issue = 12 | pages =4523–4533 | date = December 2019 | pmid = 31830850 | doi = 10.1044/2019_JSLHR-H-19-0183 | s2cid = 209342577 }} to estimate credible parameter values and model evidence given the available data and prior knowledge.

File:JASP world map.png

The following analyses are available in JASP in comparison to SPSS:

• R syntax editing and highlighting.
• Plot and formula (LaTeX) editing.
• Exports results as PDF or HTML and tables as LaTeX format.; plots as PNG, PPTX (Powerpoint) etc.
• Imports Excel and SPSS files, comma-separated files etc. (.xls, xlsx, .csv, .txt, .tsv, .ods, .dta, .sav, .zsav, .por, .sas7bdat, .sas7bcat, .xpt, .jasp)
• Connects to SQL data bases and the Open Science Framework.
• Data filtering: Use either R code or a drag-and-drop GUI to select cases of interest.
• Full data editing with one-click recoding; full undo / redo functionality,
• Compute columns via R code (e.g. via row-wise functions like rowMean, rowMeanNaRm, rowSum, rowSD ...) or a drag-and-drop GUI to create new variables or compute them from existing ones.
• Empty values settings per variable, per data set or globally.
• Assumption checks via export and then plotting of residuals and/or per analyses via tests and plots (Levene's, Brown-Forsythe, Shapiro–Wilk, Q–Q, Raincloud etc.)

JASP features seven common modules that are enabled by default:

1. Descriptives: Explore the data with tables and plots.
2. T-Tests: Evaluate the difference between two means.
3. ANOVA: Evaluate the difference between multiple means.
4. Mixed Models: Evaluate the difference between multiple means with random effects.
5. Regression: Evaluate the association between variables.
6. Frequencies: Analyses for count data.
7. Factor: Explore hidden structure in the data.

JASP also features multiple additional modules that can be activated via the module menu:

1. Acceptance Sampling: Methods for acceptance sampling and a quality control setting.
2. Audit: Statistical methods for auditing. The audit module offers planning, selection and evaluation of statistical audit samples, methods for data auditing (e.g., Benford’s law) and algorithm auditing (e.g., model fairness).
3. Bain: Bayesian informative hypotheses evaluation{{Cite journal|last1=Gu|first1=Xin|last2=Mulder|first2=Joris|last3=Hoijtink|first3=Herbert|date=2018|title=Approximated adjusted fractional Bayes factors: A general method for testing informative hypotheses|journal=British Journal of Mathematical and Statistical Psychology|language=en|volume=71|issue=2|pages=229–261|doi=10.1111/bmsp.12110|pmid=28857129|issn=2044-8317|doi-access=free}} for t-tests, ANOVA, ANCOVA, linear regression and structural equation modeling.
4. BSTS: Bayesian take on linear Gaussian state space models suitable for time series analysis.
5. Circular Statistics: Basic methods for directional data.
6. Cochrane meta-analyses: Analyse Cochrane medical datasets.
7. Distributions: Visualise probability distributions and fit them to data.
8. Equivalence T-Tests: Test the difference between two means with an interval-null hypothesis.
9. JAGS: Implement Bayesian models with the JAGS program for Markov chain Monte Carlo.
10. Learn Bayes: Learn Bayesian statistics with simple examples and supporting text.
11. Learn Stats: Learn classical statistics with simple examples and supporting text.
12. Machine Learning: Explore the relation between variables using data-driven methods for supervised learning and unsupervised learning. The module contains 19 analyses for regression, classification and clustering:
13. *Regression
14. *#Boosting Regression
15. *#Decision Tree Regression
16. *#K-Nearest Neighbors Regression
17. *#Neural Network Regression
18. *#Random Forest Regression
19. *#Regularized Linear Regression
20. *#Support Vector Machine Regression
21. *Classification
22. *#Boosting Classification
23. *#Decision Tree Classification
24. *#K-Nearest Neighbors Classification
25. *#Neural Network Classification
26. *#Linear Discriminant Classification
27. *#Random Forest Classification
28. *#Support Vector Machine Classification
29. *Clustering
30. *#Density-Based Clustering
31. *#Fuzzy C-Means Clustering
32. *#Hierarchical Clustering
33. *#Model-based clustering
34. *#Neighborhood-based Clustering (i.e., K-Means Clustering, K-Medians clustering, K-Medoids clustering)
35. *#Random Forest Clustering
36. Meta Analysis: Synthesise evidence across multiple studies. 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.
37. Network: Explore the connections between variables organised as a network. Network Analysis allows the user to analyze the network structure.
38. Power: Conduct power analyses.
39. Predictive Analytics: This module offers predictive analytics.
40. Process: Implementation of Hayes' popular SPSS PROCESS module for JASP
41. Prophet: A simple model for time series prediction.
42. Quality Control: Investigate if a manufactured product adheres to a defined set of quality criteria.
43. Reliability: Quantify the reliability of test scores.
44. Robust T-Tests: Robustly evaluate the difference between two means.
45. SEM (Structural equation modeling): Evaluate latent data structures with Yves Rosseel's lavaan program.{{Cite book|url=https://books.google.com/books?id=Q61ECgAAQBAJ&q=Principles+and+practice+of+structural+equation+modeling.&pg=PP1|title=Principles and Practice of Structural Equation Modeling, Fourth Edition|last=Kline|first=Rex B.|date=2015-11-03|publisher=Guilford Publications|isbn=9781462523351|language=en}}
46. Summary statistics: Apply common Bayesian tests from frequentist summary statistics for t-test, regression, and binomial tests.
47. Survival Analyses: non- & semi-parametric
48. Time Series: Time series analysis.
49. Visual Modeling: Graphically explore the dependencies between variables.
50. R Console: Execute R code in a console.

{{Reflist}}

• {{Official website|https://jasp-stats.org/}}
• {{GitHub|jasp-stats/jasp-desktop}}

{{DEFAULTSORT:JASP}}

Category:Free Bayesian statistics software

Category:Free educational software

Category:Free statistical software

Category:Software using the GNU Affero General Public License