Title: Adaptive Sampling for Positive Unlabeled and Label Noise
Description: Implements the adaptive sampling procedure, a framework for both positive unlabeled learning and learning with class label noise. Yang, P., Ormerod, J., Liu, W., Ma, C., Zomaya, A., Yang, J. (2018) <doi:10.1109/TCYB.2018.2816984>.
Packaged: 2018-06-01 22:01:42 UTC; pengyiyang

Packaged: 2018-06-27 15:26:54 UTC; pengyiyang

Packaged: 2019-05-21 08:02:40 UTC; pengyiyang

Title: A class of adaptive elastic net methods for censored data
Author: Hasinur Rahaman Khan and Ewart Shaw
Maintainer: Hasinur Rahaman Khan <hasinurkhan@gmail.com>
Depends: imputeYn, glmnet, lars, R (>= 2.14.2)
Description: The package provides new approaches to variable selection for censored data, based on AFT models optimized using regularized weighted least squares. Namely, a weighted elastic net, an adaptive elastic net, and two of their extensions by adding censoring observations as constraints into their model optimization frameworks.

Title: A Class of Adaptive Elastic-net Methods for Censored Data
Depends: imputeYn, glmnet, lars, quadprog, R (>= 3.0.2)
Description: Provides new approaches to variable selection for censored data including its high-dimensionality, based on AFT models optimized using regularized weighted least squares. Approaches namely, a weighted elastic net, an adaptive elastic net, and two of their extensions by adding censoring observations as constraints into their model optimization frameworks are provided with both simulated and real (MCL) data examples.

Title: A Class of Adaptive Elastic Net Methods for Censored Data
Description: Provides new approaches to variable selection for AFT model.

Description: For distributions whose probability density functions are log-concave, Adaptive Rejection Sampling (ARS) algorithm can be used to build for sampling by Gilks W R, Wild P (1992) <doi:10.2307/2347565>. For others, we can use Modifed Adaptive Rejection Sampling (MARS) algorithm by Martino L, Míguez J (2011) <doi:10.1007/s11222-010-9197-9>, Concave-Convex Adaptive Rejection (CCARS) Sampling algorithm by Görür D, Teh Y W (2011) <doi:10.1198/jcgs.2011.09058> and Adaptive Slice Sampling (ASS) algorithm by Radford M. Neal (2003) <doi:10.1214/aos/1056562461>. So we designed an R package including mainly 4 functions: rARS(), rMARS(), rCCARS(), and rASS(). These functions can realize sampling based on the algorithms above.