Imports: Rcpp, mirt (>= 1.7), lavaan (>= 0.5-17), WrightMap (>= 1.1),
tensor, sfsmisc, GPArotation, psych, lattice, mvtnorm, msm
Packaged: 2015-01-15 19:55:44 UTC; robitzsch

Imports: Rcpp, mirt, lavaan, WrightMap, tensor, sfsmisc, GPArotation,
Packaged: 2015-02-24 08:16:18 UTC; robitzsch

Packaged: 2015-04-03 09:05:00 UTC; robitzsch

Packaged: 2015-06-05 16:49:10 UTC; robitzsch

Packaged: 2015-06-26 17:54:25 UTC; robitzsch

Imports: stats, graphics, utils, Rcpp, lavaan, WrightMap, tensor,
sfsmisc, GPArotation, psych, mvtnorm, msm, lattice, plyr
Packaged: 2015-06-30 08:25:07 UTC; robitzsch

Alexander Robitzsch [aut, cre], Thomas Kiefer [aut], Margaret Wu [aut]
methods, mvtnorm, plyr, psych, Rcpp, sfsmisc, stats, utils,

Includes marginal maximum likelihood estimation and joint maximum
likelihood estimation for unidimensional and multidimensional
item response models. The package functionality covers the
Rasch model, 2PL model, 3PL model, generalized partial credit model,
multi-faceted Rasch model, nominal item response model,
structured latent class model, mixture distribution IRT models,
and located latent class models. Latent regression models and
plausible value imputation are also supported. For details see
Imports: coda, graphics, grDevices, MASS, methods, mvtnorm, Rcpp,
Suggests: GPArotation, lattice, lavaan, LSAmitR, miceadds, plyr, psych,

Imports: coda, GPArotation, graphics, grDevices, lavaan, lattice, MASS,

Imports: coda, graphics, grDevices, methods, mvtnorm, Rcpp, sfsmisc,

Imports: graphics, methods, Rcpp, stats, utils
Suggests: coda, GPArotation, grDevices, lattice, lavaan, MASS,
miceadds, mvtnorm, plyr, psych, sfsmisc, sirt, splines,

miceadds, mvtnorm, plyr, psych, sfsmisc, splines, WrightMap

Title: A Two-Stage Approach to Maximize Interpretability of Drug
Response Models Based on Multiple Molecular Data Types
Description: A two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, in press), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.