An algorithm for flexible conditional density estimation based on application of pooled hazard regression to an artificial repeated measures dataset constructed by discretizing the support of the outcome variable. To facilitate non/semiparametric estimation of the conditional density, the highly adaptive lasso, a nonparametric regression function shown to reliably estimate a large class of functions at a provably fast convergence rate with only mild assumptions, is utilized. The pooled hazards data augmentation formulation implemented was first described by Díaz and van der Laan (2011) <doi:10.2202/15574679.1356>.
Package details 


Maintainer  Nima Hejazi <nh@nimahejazi.org> 
License  MIT + file LICENSE 
Version  0.1.0 
URL  https://github.com/nhejazi/haldensify 
Package repository  View on GitHub 
Installation 
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