Department of Statistics
University of Wisconsin-Madison
oem fits various penalized regression models using the Orthogonalizing EM algorithm. It is highly efficient for tall data. It provides estimation for penalties including the lasso, MCP, SCAD, elastic net, and group lasso.
The vennLasso package is motivated by the need to address population heterogeneity in hospital system-wide risk modeling applications, however it can be used in a wide variety of settings. The vennLasso package is to be used for high-dimensional modeling scenarios where heterogeneity is defined by several binary factors which stratify the population into multiple subpopulations. For example, vennLasso can be used in a hospital-wide risk modeling application if covariate effects in risk models differ for subpopulations of patients with different chronic conditions. Here the chronic conditions are the binary stratifying factors. The vennLasso provides computation for a variable selection method which yields variable selection patterns which adhere to the hierarchical nature of the relationships between the various subpopulations.
The personalized package provides functions for fitting and validation of subgroup identification and personalized medicine models under the general subgroup identification framework of Chen et al. (2017). This package is intended for use for both randomized controlled trials and observational studies.
This visualization allows for close inspection of a comparison of two estimating equations. One is the non-smooth Gehan-weighted estimating equation of the semiparametric accelerated failure time model and the other is a smooth approximation of it.