missForest, e1071, WGCNA, ComICS, pheatmap, doParallel,
quantmod, preprocessCore, pcaMethods, foreach, DeconRNASeq,
nnls, ranger

missForest, e1071, WGCNA, ComICS, pheatmap, doParallel,
quantmod, preprocessCore, pcaMethods, foreach, nnls, ranger

Existing adaptive design methods in clinical trials. The package
includes power, stopping boundaries (sample size) calculation functions for
two-group group sequential designs, adaptive design with coprimary endpoints,
biomarker-informed adaptive design, etc.

A Package for Analysis of Accelerated Destructive Degradation
Test Data
Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the collected data, a thermal index (TI) is estimated. The TI can be useful for material rating and comparison. This package performs the least squares (LS) and maximum likelihood (ML) procedures for estimating TI for polymeric materials. The LS approach is a two-step approach that is currently used in industrial standards, while the ML procedure is widely used in the statistical literature. The ML approach allows one to do statistical inference such as quantifying uncertainties in estimation, hypothesis testing, and predictions. Two publicly available datasets are provided to allow users to experiment and practice with the functions.

Analysis of Accelerated Destructive Degradation Test Data
Yili Hong, Yimeng Xie, Zhongnan Jin, and Caleb King
Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the collected data, a thermal index (TI) is estimated. The TI can be useful for material rating and comparison. This package implements the traditional method based on the least-squares method, the parametric method based on maximum likelihood estimation, and the semiparametric method based on spline methods, and the corresponding methods for estimating TI for polymeric materials. The traditional approach is a two-step approach that is currently used in industrial standards, while the parametric method is widely used in the statistical literature. The semiparametric method is newly developed. Both the parametric and semiparametric approaches allow one to do statistical inference such as quantifying uncertainties in estimation, hypothesis testing, and predictions. Publicly available datasets are provided illustrations. More details can be found in Jin et al. (2017).