Package: MIPHENO
Version: 1.2
Date: 2011-11-01
Title: Mutant Identification through Probabilistic High throughput
Enabled NOrmalization
Author: Shannon M. Bell <bell.shannonm@gmail.com>, Lyle D. Burgoon
<burgoon.lyle@epa.gov>
Maintainer: Shannon M. Bell <bell.shannonm@gmail.com>
Depends: R (>= 2.12.1)
Imports: doBy, gdata
LazyLoad: yes
Description: This package contains functions to carry out processing of
high throughput data analysis and detection of putative
hits/mutants.  Contents include a function for post-hoc quality
control for removal of outlier sample sets, a median-based
normalization method for use in datasets where there are no
explicit controls and where most of the responses are of the
wildtype/no response class (see accompanying paper).  The
package also includes a way to prioritize individuals of
interest using am empirical cumulative distribution function.
Methods for generating synthetic data as well as data from the
Chloroplast 2010 project are included.
License: GPL (>= 3)
Packaged: 2012-01-26 18:47:08 UTC; shannon
Repository: CRAN
Date/Publication: 2012-01-27 11:27:41

Title: Measurement Invariance Assessment Using Random Effects Models
Description: Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.

Title: Multiple Imputation Random Lasso for Variable Selection with
Author: Ying Liu, Yuanjia Wang, Yang Feng, Melanie M. Wall
Description: Implements a variable selection and prediction method for high-dimensional data with missing entries following the paper Liu et al. (2016) <doi:10.1214/15-AOAS899>. It deals with missingness by multiple imputation and produces a selection probability for each variable following stability selection. The user can further choose a threshold for the selection probability to select a final set of variables. The threshold can be picked by cross validation or the user can define a practical threshold for selection probability. If you find this work useful for your application, please cite the method paper.