Package: VCA
Version: 1.4.5
Date: 2022-09-07
Title: Variance Component Analysis
Author: Andre Schuetzenmeister [aut, cre], Florian Dufey [aut]
Maintainer: Andre Schuetzenmeister <andre.schuetzenmeister@roche.com>
Depends: R (>= 3.0.0)
Imports: stats, graphics, grDevices, lme4, Matrix, methods, numDeriv
Suggests: VFP, STB, knitr, rmarkdown, prettydoc
Description:
ANOVA and REML estimation of linear mixed models is implemented, once following
Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package.
The primary objective of this package is to perform a variance component analysis (VCA)
according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement
Procedures" (2014). There are plotting methods for visualization of an experimental design,
plotting random effects and residuals. For ANOVA type estimation two methods for computing
ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of
variance components can be derived, which is used in estimating confidence intervals. Linear
hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific
values of covariables and with custom weighting schemes for factor variables. See ?VCA for a
more comprehensive description of the features.
License: GPL (>= 3)
VignetteBuilder: knitr, rmarkdown
NeedsCompilation: yes
Packaged: 2022-09-07 08:31:50 UTC; schueta6
Repository: CRAN
Date/Publication: 2022-09-07 09:30:02 UTC

Author: Edward Grant, Xia Li, Kenneth Lange, Tong Tong Wu
Description: Multicategory Vertex Discriminant Analysis

Author: Edward Grant, Xia Li,  Kenneth Lange, Tong Tong Wu
Description: Multicategory Vertex Discriminant Analysis: A novel supervised multicategory classification method

Description: Analyze Peptide Array Data and characterize peptide
sequence space. Allows for high level visualization of global signal, Quality control based
on replicate correlation and/or relative Kd, calculation of peptide Length/Charge/Kd parameters,
Hits selection based on RFU Signal, and amino acid composition/basic motif recognition with RFU
signal weighting. Basic signal trends can be used to generate peptides that follow the observed
Imports: stringr, drc, ggplot2, reshape2