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mbir

mbir: Magnitude-Based Inferences - Published in JOSS (2019)

https://github.com/kdpeterson51/mbir

Science Score: 95.0%

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  • Host: GitHub
  • Owner: kdpeterson51
  • License: gpl-2.0
  • Language: HTML
  • Default Branch: master
  • Size: 1.85 MB
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Created almost 9 years ago · Last pushed over 5 years ago
Metadata Files
Readme License

README.md

version status CRAN Build Status

mbir: Magnitude-Based Inferences

Kyle D Peterson and Aaron R Caldwell

Overview

Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. A major goal of mbir is to programmatically detect appropriate statistical tests to run in lieu of relying on practitioners to determine correct stepwise procedures independently.

Installation

Assuming users have R downloaded and installed, mbir can be installed right from CRAN by typing:

install.packages("mbir") This package depends on the packages graphics, stats, and utils, which are imported upon installation.

A brief walk through mbir

Below is an example of how a user would perform a t-test with the classic Sleep data set. data('sleep') mbir::smd_test(sleep$extra[sleep$group==1],sleep$extra[sleep$group==2],paired = F) By default, smd_test function tested for normality and homogeneity prior to performing the respectgive t-test. The output first states the results of the preliminary assumption tests, followed by the t-test parameters, which are then used to calculate the appropriate effect size estimate (in this case Cohen's d). Lastly, smd_test prints the magnitude-based inference about the effect size estimate by providing the partitioned probabilities and associated qualitative label.

Below is an example of how a user would perform a correlation with the classic mtcars data set, which provide the same theme as above. data('mtcars') mbir::corr_test(mtcars$mpg,mtcars$qsec) For a detailed exploration of mbir, please visit our vignette.

Feedback

Feedback from users is welcome, and would be sincerely appreciated, to help improve functionality of mbir where warranted. Please reach out to petersonkdon@gmail.com for support, reporting issues, or contributions. Thank you very much.

Owner

  • Name: Kyle D Peterson
  • Login: kdpeterson51
  • Kind: user

JOSS Publication

mbir: Magnitude-Based Inferences
Published
January 22, 2019
DOI
Volume 4, Issue 33, Page 746
Authors
Kyle D. Peterson ORCID
Sports Science, University of Iowa
Aaron R. Caldwell ORCID
Exercise Science Research Center, University of Arkansas
Editor
Arfon Smith ORCID
Tags
statistics
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Dependencies

mbir/DESCRIPTION cran
  • effsize * imports
  • graphics * imports
  • psych * imports
  • stats * imports
  • utils * imports
  • knitr * suggests
  • rmarkdown * suggests
  • testthat * suggests