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writR: is an R package for automated inferential testing (for group differences) and reporting based on parametric assumptions, which are tested automatically for test selection.

Home Page: https://matcasti.github.io/writR

License: GNU General Public License v3.0

R 100.00%
anova paired-samples independent-samples rank-sums parametric t-test wilcoxon kruskal-wallis robust friedman

writr's Introduction

Hi there ๐Ÿ‘‹

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By day I am an assistant professor in the Kinesiology Department at the Universidad de Magallanes and a researcher in the Austral Chilean Integrative Molecular Neurophysiology group (NIM-ACh). By night an enthusiastic R programmer and web developer, with a taste for stochastic processes and bayesian modeling.

๐Ÿ”ญ Research

Iโ€™m currently working on sports and exercise physiology research, focused on clinical contexts. You may see my work on ResearchGate, ORCID ORCID, or Google Scholar.

โšก Software Development

Software development creating R packages for statistical computing, reproducible research and data science.

  • writR, for manuscript-ready statistical computing with assumptions being tested (primarily for group differences).
  • dtPipe, pipe-able functions for using with data.table objects.
  • labinstrumentos, processed data from the Instruments Laboratory of the Ministry of Science (Chile), as well as tools for analysis and visualisation.
  • rlockdown, allow users to protect their standalone html files in an easy manner with no server-side comunication.
  • mammals, tools for population dynamics analysis.
  • prices, helper functions for services prices estimation based on time-dependent work.
  • youngSwimmers, data-package with records used in the young elite swimmers study.

Contact

For contact purposes, I prefer to receive e-mails to [email protected]

writr's People

Contributors

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Watchers

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writr's Issues

Why use Levenne's Test for variance difference testing?

In writR, variance testing is done with Levenne's Test. However, as far as I understand, Levenne's Test is more prone to type-II-error, which means it more often does not detect an actual difference in variances. Why not use F-Test or Bartlett? I'd rather use a corrective method too often (since Levenne's Test has a lower Type-I-error) than not correct for an actual difference in variances - or am I missing something?

Fore-coming improvements

Upgrades/changes for next update (v.1.1)

  • add summary method for class writR
  • add convention for effect sizes (as another object in the output list?)
  • #2
  • add centrality and dispersion parameters (parametric [mean, sd], and non-parametric [median, IQR]) in output from each group

one-sample t-test?

First of all: Nice package you've created! It was the first one I encountered when looking for automated assumption testing in R along with the statistical test.

My question is: Will you integrate a one-sample t-test?

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