Title: Sensitivity Analysis Optimized for Matched Sets of Varied Sizes
Description: As in music, a fugue statistic repeats a theme in small variations.  Here, the psi-function that defines an m-statistic is slightly altered to maintain the same design sensitivity in matched sets of different sizes.  The main functions in the package are sen() and senCI().  For sensitivity analyses for m-statistics, see Rosenbaum (2007) Biometrics 63 456-464 <doi:10.1111/j.1541-0420.2006.00717.x>.
Packaged: 2019-02-09 18:32:48 UTC; Rosenbaum

Title: Functions and Utilities for Jordan
Maintainer: Jordan Mark Barbone <jmbarbone@gmail.com>
Description: Provides core functions and utilities for packages and other code
Author: Jordan Mark Barbone [aut, cph, cre]

Title: Plot Full ROC Curves using Eyewitness Lineup Data
Authors@R: person("Yueran", "Yang", email = "yuerany@unr.edu", role = c("aut", "cre"))
Description: Enable researchers to adjust identification rates using the 1/(lineup size) method, generate the full receiver operating characteristic (ROC) curves, and statistically compare the area under the curves (AUC).
References: Yueran Yang & Andrew Smith. (2020). "fullROC: An R package for generating and analyzing eyewitness-lineup ROC curves". <doi:10.13140/RG.2.2.20415.94885/1>  ,
Andrew Smith, Yueran Yang, & Gary Wells. (2020). "Distinguishing between investigator discriminability and eyewitness discriminability: A method for creating full receiver operating characteristic curves of lineup identification performance". Perspectives on Psychological Science, 15(3), 589-607. <doi:10.1177/1745691620902426>.

Author: Aimee Lee Houde [aut, cre], Trevor Pitcher [aut]
Maintainer: Aimee Lee Houde <aimee.lee.houde@gmail.com>
Description: Package for the analysis of full factorial breeding designs.
BugReports: Aimee Lee Houde <aimee.lee.houde@gmail.com>

Description: We facilitate the analysis of full factorial mating designs with mixed-effects models. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap confidence intervals, which can then be plotted using a simple function. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses. The paper associated with the package including worked examples is: Houde ALS, Pitcher TE (2016) <doi:10.1002/ece3.1943>.
Packaged: 2016-12-17 16:24:58 UTC; Bert and Diane