Package: VRPM
Type: Package
Title: Visualizing Risk Prediction Models
Version: 1.2
Date: 2017-07-11
Author: Vanya Van Belle
Maintainer: Vanya Van Belle <vanya.vanbelle@esat.kuleuven.be>
Description: This is a package to visualize risk prediction models. For each
predictor, a color bar represents the contribution to the linear predictor or
latent variable. A conversion from the linear predictor to the estimated risk
or survival is also given. (Cumulative) contribution charts enable to visualize
how the estimated risk for one particular observation is obtained by the model.
Several options allow to choose different color maps, and to select the zero
level of the contributions. The package is able to deal with 'glm', 'coxph',
'mfp', 'multinom' and 'ksvm' objects. For 'ksvm' objects, the visualization
is not always exact. Functions providing tools to indicate the accuracy of the
approximation are provided in addition to the visualization.
Imports: Hmisc, survival, fields, R2HTML, viridis, kernlab, ROCR,
ggplot2, shiny, methods, stats, grDevices, graphics, utils
Suggests: mfp, VGAMdata, nnet
License: GPL-3
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2017-07-11 09:01:33 UTC; vanya
Repository: CRAN
Date/Publication: 2017-07-11 10:42:14 UTC

Description: Calculates the enrichment of associated variant set (AVS) for an array of genomic regions. The AVS is the collection of disjoint LD blocks computed from a list of disease associated SNPs and their linked (LD) SNPs. VSE generates a null distribution of matched random variant sets (MRVSs) from 1000 Genome Project Phase III data that are identical to AVS, LD block by block. It then computes the enrichment of AVS intersecting with user provided genomic features (e.g., histone marks or transcription factor binding sites) compared with the null distribution.
Packaged: 2016-03-21 18:34:41 UTC; musaahmed

Title: Variable Selection Oriented LASSO Bagging Algorithm
person('Jiaqi', 'Liang',  email = 'liangjq27@mail2.sysu.edu.cn', role = c('aut')),
person('Chaoye', 'Wang',  email = 'wangcy1@sysucc.org.cn', role = c('aut', 'cre'))
Description: A wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient, stable, and high confidential variables from omics-based data. Using a bagging strategy in combination of a parametric method or inflection point search method for cut-off threshold determination. This package can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates. Luo H, Zhao Q, et al (2020) <doi:10.1126/scitranslmed.aax7533> for more details.
Imports: glmnet, survival, ggplot2, POT, parallel, utils, pbapply,
Suggests: rmarkdown, knitr, rmdformats, qpdf