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R

Script-based methods in study design and analysis support transparency of analytical methods.  PAASP invites all colleagues to share their examples of R scripts, links to useful R packages, literature and training resources.

R packages

bootES (“bootstrap Effect Sizes”) finds bootstrap confidence intervals for effect sizes for mean effects, mean differences, contrasts, correlations, and differences between correlations.

blockrand (“block randomization”) creates block randomization schedules with or without stratification.

TOSTER (“Two One-Sided Tests (TOST) Equivalence Testing”) enables testing of equivalence for t-tests, correlations, differences between proportions, and meta-analyses, including power analysis for t-tests and correlations.

crossdes (“construction of Crossover Designs”) contains functions for the construction of carryover balanced crossover designs (such as Williams or Latin square). In addition contains functions to check given designs for balance.

R for beginners

A little book of R for biomedical statistics, by Avril Coghlan (2015)

Introductory R, by Robert J Knell (2014)

Learn to use R, by Sharon Machlis (2015)

Report writing for data science in R, by Roger D Peng (2016)

R for beginners, by Emmanuel Paradis (2005)

DataCamp provides easy-to-use and interactive training

R scripts

Power analysis for two-sided t-test

Resources

The R project and the CRAN site – getting started information, FAQs, packages, manuals and much more …

RStudio – a free and open-source integrated development environment for R. It makes R easier to use. RStudio includes a code editor, debugging & visualization tools

R documentation, R programming.net and R-bloggers can be searched for an already existing answer to a question (e.g. “how to”) or can be used to post a new question to the R expert community


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