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Jasp Software Mac

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JASP version:0.11.1 OS name and version: MacOS 10.14.6 Analysis:logistic regression Bug description:after successfully uploading my dataset, I clicked on logistic regression within the regression icon and I got the message saying that du. Minitool partition wizard 4k.

  • JASP is an open-source project supported by the University of Amsterdam.
  • MeMoFinder is a software helping researchers in the task of finding motifs in biological sequences using a consensus approach.

Quoting the JASP website,

JASP is an open-source project supported by the University of Amsterdam.

Jasp Manual

Jasp software analysis

Jasp Software Mac Pro

JASP has an intuitive interface that was designed with the user in mind.

Jasp software mac os

JASP offers standard analysis procedures in both their classical and Bayesian form. Job description business analyst.

JASP itself consists of two different executables which are licensed under slightly different terms.
The JASP-Engine, where our R code runs, is distributed under GNU GPLv2
But the JASP-Desktop, the user interface, is distributed under GNU Affero GPL v3

JASP
Stable release
RepositoryJASP Github page
Written inC++, R, JavaScript
Operating systemMicrosoft Windows, Mac OS X and Linux
TypeStatistics
LicenseGNU Affero General Public License
Websitejasp-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.

JASP screenshot

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:

AnalysisFrequentistBayesian
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]

Jasp Software Reviews

  • 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]

  1. Summary statistics: Bayesian inference from frequentist summary statistics for t-test, regression, and binomial tests.
  2. BAIN: Bayesian informative hypotheses evaluation[7] 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. Random Forest Regression
      3. Regularized Linear Regression
    • Classification
      1. K-Nearest Neighbors Classification
      2. Linear Discriminant Classification
    • Clustering
  6. SEM: Structural equation modeling.[8]
  7. JAGS module
  8. Discover distributions
  9. Equivalence testing

Jasp Software Mac Os

Mac

Jasp Software Mac Free

References[edit]

  1. ^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.
  2. ^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).
  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.
  4. ^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.
  5. ^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.
  6. ^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.
  7. ^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.
  8. ^Kline, Rex B. (2015-11-03). Principles and Practice of Structural Equation Modeling, Fourth Edition. Guilford Publications. ISBN9781462523351.
Jasp

Jasp Software Mac Pro

JASP has an intuitive interface that was designed with the user in mind.

JASP offers standard analysis procedures in both their classical and Bayesian form. Job description business analyst.

JASP itself consists of two different executables which are licensed under slightly different terms.
The JASP-Engine, where our R code runs, is distributed under GNU GPLv2
But the JASP-Desktop, the user interface, is distributed under GNU Affero GPL v3

JASP
Stable release
RepositoryJASP Github page
Written inC++, R, JavaScript
Operating systemMicrosoft Windows, Mac OS X and Linux
TypeStatistics
LicenseGNU Affero General Public License
Websitejasp-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.

JASP screenshot

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:

AnalysisFrequentistBayesian
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]

Jasp Software Reviews

  • 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]

  1. Summary statistics: Bayesian inference from frequentist summary statistics for t-test, regression, and binomial tests.
  2. BAIN: Bayesian informative hypotheses evaluation[7] 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. Random Forest Regression
      3. Regularized Linear Regression
    • Classification
      1. K-Nearest Neighbors Classification
      2. Linear Discriminant Classification
    • Clustering
  6. SEM: Structural equation modeling.[8]
  7. JAGS module
  8. Discover distributions
  9. Equivalence testing

Jasp Software Mac Os

Jasp Software Mac Free

References[edit]

  1. ^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.
  2. ^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).
  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.
  4. ^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.
  5. ^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.
  6. ^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.
  7. ^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.
  8. ^Kline, Rex B. (2015-11-03). Principles and Practice of Structural Equation Modeling, Fourth Edition. Guilford Publications. ISBN9781462523351.

External links[edit]

Jasp Software Mac Download

  • jasp-desktop on GitHub

Jasp Software Analysis

Retrieved from 'https://en.wikipedia.org/w/index.php?title=JASP&oldid=998715328'




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