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HQM (version 0.1.2)

Superefficient Estimation of Future Conditional Hazards Based on Marker Information

Description

Provides a nonparametric smoothed kernel density estimator for the future conditional hazard when time-dependent covariates are present. It also provides pointwise and uniform confidence bands and a bandwidth selection.

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Version

Install

install.packages('HQM')

Monthly Downloads

233

Version

0.1.2

License

GPL (>= 2)

Maintainer

Dimitrios Bagkavos

Last Published

December 4th, 2024

Functions in HQM (0.1.2)

llK_b

Local linear kernel
make_sf

Survival function from a hazard
prep_boot

Precomputation for wild bootstrap
llweights

Local linear weight functions
get_h_xll

Local linear future conditional hazard rate estimator
prep_cv

Prepare for Cross validation bandwidth selection
pbc2

Mayo Clinic Primary Biliary Cirrhosis Data
to_id

Event data frame
make_N, make_Ni, make_Y, make_Yi

Occurance and Exposure on grids
Q1

Bandwidth selection score Q1
b_selection_prep_g

Preparations for bandwidth selection
auc.hqm

AUC for the High Quality Marker estimator
b_selection

Cross validation bandwidth selection
Kernels

Classical (unmodified) kernel and related functionals
bs.hqm

Brier score for the High Quality Marker estimator
R_K

Bandwidth selection score R
Conf_bands

Confidence bands
Epan

Epanechnikov kernel
get_alpha

Marker-only hazard rate
h_xtll

Local linear future conditional hazard rate estimation at a single time point
h_xt_vec

Hqm estimator on the marker grid
dataset_split

Split dataset for K-fold cross validation
dij

D matrix entries, used for the implementation of the local linear kernel
h_xt

Local constant future conditional hazard rate estimation at a single time point
g_xt

Computation of a key component for wild bootstrap
lin_interpolate

Linear interpolation
get_h_x

Local constant future conditional hazard rate estimator