Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

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GENEAclassify — by Charles Sweetland, a year ago

Segmentation and Classification of Accelerometer Data

Segmentation and classification procedures for data from the 'Activinsights GENEActiv' < https://www.activinsights.com/products/geneactiv/> accelerometer that provides the user with a model to guess behaviour from test data where behaviour is missing. Includes a step counting algorithm, a function to create segmented data with custom features and a function to use recursive partitioning provided in the function rpart() of the 'rpart' package to create classification models.

mangoTraining — by Andrew Little, a year ago

Mango Solutions Training Datasets

Datasets to be used primarily in conjunction with Mango Solutions training materials but also for the book 'SAMS Teach Yourself R in 24 Hours' (ISBN: 978-0-672-33848-9). Version 1.0-7 is largely for use with the book; however, version 1.1 has a much greater focus on use with training materials, whilst retaining compatibility with the book.

fullfact — by Aimee Lee Houde, a year ago

Full Factorial Breeding Analysis

We facilitate the analysis of full factorial mating designs with mixed-effects models. There are now six vignettes containing detailed examples.

broman — by Karl W Broman, 7 months ago

Karl Broman's R Code

Miscellaneous R functions, including functions related to graphics (mostly for base graphics), permutation tests, running mean/median, and general utilities.

salbm — by Aidan McDermott, a year ago

Sensitivity Analysis for Binary Missing Data

In a clinical trial with repeated measures designs, outcomes are often taken from subjects at fixed time-points. The focus of the trial may be to compare the mean outcome in two or more groups at some pre-specified time after enrollment. In the presence of missing data auxiliary assumptions are necessary to perform such comparisons. One commonly employed assumption is the missing at random assumption (MAR). The 'salbm' package allows the user to perform a (parameterized) sensitivity analysis of this assumption where the outcome of interest is binary (coded as 0, 1, or NA). In particular it can be used to examine the sensitivity of tests in the difference in outcomes to violations of the MAR assumption. See the paper Daniel O. Scharfstein, Jaron J. R. Lee, Aidan McDermott, Aimee Campbell, Edward Nunes, Abigail G. Matthews, Ilya Shpitser "Markov-Restricted Analysis of Randomized Trials with Non-Monotone Missing Binary Outcomes: Sensitivity Analysis and Identification Results" (2021) .