| Name : R-haldensify
| |
| Version : 0.2.3
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.9.2
| Date : 2024-06-14 11:31:00
|
| Group : Development/Libraries/Other
| Source RPM : R-haldensify-0.2.3-lp153.9.2.src.rpm
|
| Size : 0.21 MB
| |
| Packager : (none)
| |
| Summary : Highly Adaptive Lasso Conditional Density Estimation
|
Description :
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/semi-parametric estimation of the
conditional density, the highly adaptive lasso, a nonparametric
regression function shown to reliably estimate a large class of
functions at a fast convergence rate, is utilized. The pooled hazards
data augmentation formulation implemented was first described by Díaz
and van der Laan (2011) < doi:10.2202/1557-4679.1356>. To complement the
conditional density estimation utilities, tools for efficient
nonparametric inverse probability weighted (IPW) estimation of the
causal effects of stochastic shift interventions (modified treatment
policies), directly utilizing the density estimation technique for
construction of the generalized propensity score, are provided. These
IPW estimators utilize undersmoothing (sieve estimation) of the
conditional density estimators in order to achieve the
non/semi-parametric efficiency bound.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
