JASP offers a fresh way to do statistics. The application is a low-fat alternative to SPSS, and a perfect alternative to R. Bayesian statistics made accessible. The JASP application is written in C, using the Qt toolkit. The analyses themselves are written in either R or C. The JasperReports Library is the world's most popular open source reporting engine. It is entirely written in Java and it is able to use data coming from any kind of data source and produce pixel-perfect documents that can be viewed, printed or exported in a variety of document formats including HTML, PDF, Excel, OpenOffice and Word.
| Stable release | |
|---|---|
| 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 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.[1][2] 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.
Analyses[edit]
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[3][4] 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 | ||
| ANOVA, ANCOVA, Repeated measures ANOVA and MANOVA | ||
| AUDIT (module) | ||
| Bain (module) | ||
| Binomial test | ||
| Confirmatory factor analysis (CFA) | ||
| Contingency tables (including Chi-squared test) | ||
| Correlation:[5]Pearson, Spearman, and Kendall | ||
| Equivalence T-Tests: Independent, Paired, One-Sample | ||
| Exploratory factor analysis (EFA) | ||
| Linear regression | ||
| Logistic regression | ||
| Log-linear regression | ||
| Machine Learning | ||
| Mann-Whitney U and Wilcoxon | ||
| Mediation Analysis | ||
| Meta Analysis | ||
| Mixed Models | ||
| Multinomial test | ||
| Network Analysis | ||
| Principal component analysis (PCA) | ||
| Reliability analyses: α, γδ, and ω | ||
| Structural equation modeling (SEM) | ||
| Summary Stats[6] | ||
| T-tests: independent, paired, one-sample | ||
| Visual Modeling: Linear, Mixed, Generalized Linear |
Other features[edit]
- Descriptive statistics and plots.
- 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.
- PDF export of results.
Modules[edit]
- Summary statistics: Bayesian inference from frequentist summary statistics for t-test, regression, and binomial tests.
- BAIN: Bayesian informative hypotheses evaluation[7] for t-test, ANOVA, ANCOVA and linear regression.
- Network: Network Analysis allows the user to analyze the network structure of variables.
- 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.
- Machine Learning: Machine Learning module contains 13 analyses for supervised an unsupervised learning:
- Regression
- Boosting Regression
- Random Forest Regression
- Regularized Linear Regression
- Classification
- K-Nearest Neighbors Classification
- Linear Discriminant Classification
- Clustering
- Regression
- SEM: Structural equation modeling.[8]
- JAGS module
- Discover distributions
- Equivalence testing
References[edit]
- ^Wagenmakers EJ, Love J, Marsman M, Jamil T, Ly A, Verhagen J, et al. (February 2018). 'Bayesian inference for psychology. Part II: Example applications with JASP'. Psychonomic Bulletin & Review. 25 (1): 58–76. doi:10.3758/s13423-017-1323-7. PMC5862926. PMID28685272.
- ^Love J, Selker R, Verhagen J, Marsman M, Gronau QF, Jamil T, Smira M, Epskamp S, Wil A, Ly A, Matzke D, Wagenmakers EJ, Morey MD, Rouder JN (2015). 'Software to Sharpen Your Stats'. APS Observer. 28 (3).
- ^Quintana DS, Williams DR (June 2018). 'Bayesian alternatives for common null-hypothesis significance tests in psychiatry: a non-technical guide using JASP'. BMC Psychiatry. 18 (1): 178. doi:10.1186/s12888-018-1761-4. PMC5991426. PMID29879931.
- ^Brydges CR, Gaeta L (December 2019). 'An Introduction to Calculating Bayes Factors in JASP for Speech, Language, and Hearing Research'. Journal of Speech, Language, and Hearing Research. 62 (12): 4523–4533. doi:10.1044/2019_JSLHR-H-19-0183. PMID31830850.
- ^Nuzzo RL (December 2017). 'An Introduction to Bayesian Data Analysis for Correlations'. PM&R. 9 (12): 1278–1282. doi:10.1016/j.pmrj.2017.11.003. PMID29274678.
- ^Ly A, Raj A, Etz A, Marsman M, Gronau QF, Wagenmakers E (2017-05-30). 'Bayesian Reanalyses from Summary Statistics: A Guide for Academic Consumers'. Open Science Framework.
- ^Gu, Xin; Mulder, Joris; Hoijtink, Herbert (2018). 'Approximated adjusted fractional Bayes factors: A general method for testing informative hypotheses'. British Journal of Mathematical and Statistical Psychology. 71 (2): 229–261. doi:10.1111/bmsp.12110. ISSN2044-8317. PMID28857129.
- ^Kline, Rex B. (2015-11-03). Principles and Practice of Structural Equation Modeling, Fourth Edition. Guilford Publications. ISBN9781462523351.
External links[edit]
- jasp-desktop on GitHub
CONN includes a rich set of connectivity analyses (seed-based correlations, ROI-to-ROI graph analyses, group ICA, masked ICA, generalized PPI, ALFF, ICC, GCOR, LCOR, etc.) in a simple-to-use and powerful software package
CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing.
Highlights:
Comprehensive quality assurance methods/measures/displays (scrubbing/ART, CompCor/ICA/GSR denoising, GCOR/FD QC covariates)
Connectome-wide analyses (ICA, MVPA)
Dynamic connectivity analyses (dyn-ICA)
FWE-control of connectivity matrices (NBS)
Non-parametric cluster-level statistics (permutation tests)
ANOVA, regression, longitudinal, and mixed designs
Parallelization options (SGE/Grid Engine, PBS/Torque, LSF, Slurm)
Mac/Windows/Linux/HPC
SPM8/SPM12
http://www.conn-toolbox.org
SPM_SS - fMRI functional localizers
Artifact Detection Tools (ART)
FreeSurfer
Longitudinal Analysis Workspace
Network-Based Statistic (NBS)
HCP Harvard/MGH-UCLA
Mindfulness training preserves sustained attention and resting state anticorrelation between default-mode network and dorsolateral prefrontal cortex: A randomized controlled trial. posted by NITRC Moderator on Dec 26, 2020
Birth weight is associated with adolescent brain development: A multimodal imaging study in monozygotic twins. posted by NITRC Moderator on Dec 26, 2020
Associations Between Altered Cerebral Activity Patterns and Psychosocial Disorders in Patients With Psychogenic Erectile Dysfunction: A Mediation Analysis of fMRI. posted by NITRC Moderator on Dec 26, 2020
Autistic traits are associated with the functional connectivity of between-but not within-attention systems in the general population. posted by NITRC Moderator on Dec 26, 2020
Characteristics of resting-state functional connectivity in older adults after the PICMOR intervention program: a preliminary report. posted by NITRC Moderator on Dec 26, 2020
Functional Connectivity of Successful Picture-Naming: Age-Specific Organization and the Effect of Engaging in Stimulating Activities. posted by NITRC Moderator on Dec 26, 2020
Fusiform Activity Distinguishes Between Subjects With Low and High Xenophobic Attitudes Toward Refugees. posted by NITRC Moderator on Nov 14, 2020
Relationship between media multitasking and functional connectivity in the dorsal attention network. posted by NITRC Moderator on Nov 14, 2020
Defining data-driven subgroups of obsessive-compulsive disorder with different treatment responses based on resting-state functional connectivity. posted by NITRC Moderator on Nov 14, 2020
Unconscious reinforcement learning of hidden brain states supported by confidence. posted by NITRC Moderator on Oct 17, 2020
RE: Contrast specification for Group comparison in a multi-center RCT (controlling for scanner) posted by Marcel Daamen 9 hours ago
MEICA-preprocessed data for denoising and analysis in CONN posted by Remy Cohan 11 hours ago
RE: HPC - SLURM - Parralellisation with conn posted by sophieb 18 hours ago
Analyze linear trend for one task with PPI posted by Gustavo Pamplona 20 hours ago
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RE: ICA: number of components Cutpoints; CompCor posted by Mickael Tordjman on Jan 12
RE: HPC - SLURM - Parralellisation with conn posted by sophieb on Jan 12
Contrast specification for Group comparison in a multi-center RCT (controlling for scanner) posted by Till Langhammer on Jan 12
RE: HPC - SLURM - Parralellisation with conn posted by sophieb on Jan 12
RE: Graph theory in conn posted by Alfonso Nieto-Castanon on Jan 12
CONN new release posted by Alfonso Nieto-Castanon on Feb 18, 2020
New CONN release and tutorials posted by Alfonso Nieto-Castanon on Jun 27, 2017
CONN New Year News posted by Alfonso Nieto-Castanon on Jan 12, 2017
CONN new release (15f) posted by Alfonso Nieto-Castanon on Sep 25, 2015
Announcing Cincinnati CONN course (MGH/HST) posted by Alfonso Nieto-Castanon on Jun 9, 2015
CONN v.13n Released posted by Alfonso Nieto-Castanon on Jul 8, 2013
conn toolbox version 13 released posted by Alfonso Nieto-Castanon on Oct 31, 2011
Batch script for analyzing the NYU_CSC dataset posted by Alfonso Nieto-Castanon on Sep 16, 2009
conn20b.zip posted by Alfonso Nieto-Castanon on Dec 1, 2020
conn19c.zip posted by Alfonso Nieto-Castanon on Mar 14, 2020
mac MCR 2019b (9.7) posted by Alfonso Nieto-Castanon on Feb 11, 2020
Jasp Logiciel Mac Gratuit
conn19b_maci64.zip posted by Alfonso Nieto-Castanon on Feb 11, 2020
conn18b.zip posted by Alfonso Nieto-Castanon on Dec 19, 2018
Jasp Logiciel Mac Os
conn18b_glnxa64.zip posted by Alfonso Nieto-Castanon on Dec 19, 2018
windows MCR 2018b (9.5) posted by Alfonso Nieto-Castanon on Dec 19, 2018
conn18b_win64.zip posted by Alfonso Nieto-Castanon on Dec 19, 2018
linux MCR 2018b (9.5) posted by Alfonso Nieto-Castanon on Dec 19, 2018
conn18b_maci64.zip posted by Alfonso Nieto-Castanon on Dec 18, 2018