Enhanced Tipping Point Displays the Results of Sensitivity
Analyses for Missing Data
Shengjie Zhang <zhangshengjie@mrbc-nccd.com>,
Xikun Han <hanxikun2014@163.com> and
Victoria Liublinska <vliublin@g.harvard.edu>
Using the idea of "tipping point" (proposed in
Gregory Campbell, Gene Pennello and Lilly Yue(2011)
<DOI:10.1080/10543406.2011.550094>) to visualize  the results
of sensitivity analysis for missing data, the package provides
a set of functions to list out all the possible combinations
of the values of missing data in two treatment arms, calculate
corresponding estimated treatment effects and p values and draw
a colored heat-map to visualize them. It could deal with randomized
experiments with a binary outcome or a continuous outcome. In addition,
the package provides a visualized method to compare various imputation
methods by adding the rectangles or convex hulls on the basic plot.
ggplot2 (>= 2.0.0), RColorBrewer, bayesSurv, reshape2, Rcpp

ggplot2 (>= 2.0.0), RColorBrewer, bayesSurv, reshape2

Enhanced Tipping Point Displays the Results of Sensitivity
Analysis for Missing Data
Shengjie Zhang <zhangshengjie@mrbc-nccd.com>,
Xikun Han <hanxikun2017@gmail.com> and
Victoria Liublinska <vliublin@g.harvard.edu>
Using the idea of "tipping point" (proposed in
Gregory Campbell, Gene Pennello and Lilly Yue(2011)
<DOI:10.1080/10543406.2011.550094>) to visualize  the results
of sensitivity analysis for missing data, the package provides
a set of functions to list out all the possible combinations
of missing values in two treatment arms, calculate
corresponding estimated treatment effects and p values, and draw
a colored heat-map. It could deal with randomized
experiments with a binary outcome or a continuous outcome. In addition,
the package provides a visualized method to compare various imputation
methods by adding the rectangles or convex hulls on the basic plot.
ggplot2 (>= 2.0.0), RColorBrewer, reshape2

Non-Convex Optimization and Statistical Inference for Sparse
Tensor Graphical Models
An optimal alternating optimization algorithm for estimation of precision matrices of sparse tensor graphical models, and an efficient inference procedure for support recovery of the precision matrices.
huge, expm, rTensor, igraph, stats, graphics
Will Wei Sun, Zhaoran Wang, Xiang Lyu, Han Liu, Guang Cheng