Description: Convenience functions and datasets to be used with Practical Multilevel Modeling using R. The package includes functions for calculating group means, group mean centered variables, and displaying some basic missing data information. A function for computing robust standard errors for linear mixed models based on Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and 'McCaffrey' (2002) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ> is included as well as a function for checking for level-one homoskedasticity (Raudenbush & Bryk, 2002, ISBN:076191904X).
Imports: lme4, stats, nlme, Matrix, methods

Title: Multiplex Ligation-Dependent Probe Amplification Data Analysis
person("Sylvain", "Mareschal", role=c("aut", "cre"), email="maressyl@gmail.com"),
Description: Functions to import Applied Biosystems data files of multiplex ligation-dependent probe amplification (MLPA) analysis and process them. Gene-expression profiling methods are described in Mareschal, Ruminy et al (2015) <doi:10.1016/j.jmoldx.2015.01.007>. Gene-fusion detection methods are described in Mareschal, Palau et al (under review).
Depends: graphics, grDevices, stats, utils, R (>= 2.10)
Suggests: Biostrings, parallel, tcltk, tools

Title: MLPA analysis to detect gains and loses in genes
Depends: nlme, boot, tcltk, tkrplot, pixmap
Author: Alejandro C�ceres and Juan R Gonz�lez
Description: MLPAstats is a software designed to test copy number
differences between case and control samples, identified with
multiplex-dependent probe amplification (MLPA) data. The
package incorporates several methods for data normalization and
copy number alterations such as REX-regression, threshold
Packaged: 2011-01-31 16:10:31 UTC; jrgonzalez

Packaged: 2011-02-11 13:21:51 UTC; jrgonzalez

Title: Multi-Label Prediction Using Gibbs Sampling (and Classifier
Authors@R: person("Mikhail", "Popov", email = "mikhail@mpopov.com",
role = c("aut", "cre"), comment = "@bearloga on Twitter")
Description: An implementation of classifier chains (CC's) for multi-label
prediction. Users can employ an external package (e.g. 'randomForest',
'C50'), or supply their own. The package can train a single set of CC's or
train an ensemble of CC's -- in parallel if running in a multi-core
environment. New observations are classified using a Gibbs sampler since
each unobserved label is conditioned on the others. The package includes
methods for evaluating the predictions for accuracy and aggregating across
iterations and models to produce binary or probabilistic classifications.
Suggests: knitr, progress, C50, randomForest
Author: Mikhail Popov [aut, cre] (@bearloga on Twitter)