Package: HQM 1.0

Dimitrios Bagkavos

HQM: Superefficient Estimation of Future Conditional Hazards Based on Marker Information

Provides a nonparametric smoothed kernel estimator for the future conditional hazard rate function when time-dependent covariates are present, a bandwidth selector for the estimator's implementation and pointwise and uniform confidence bands. Methods used in the package refer to Bagkavos, Isakson, Mammen, Nielsen and Proust-Lima (2025) <doi:10.1093/biomet/asaf008>.

Authors:Dimitrios Bagkavos [aut, cre], Alex Isakson [ctb], Enno Mammen [ctb], Jens Nielsen [ctb], Cecile Proust-Lima [ctb]

HQM_1.0.tar.gz
HQM_1.0.tar.gz(r-4.5-noble)HQM_1.0.tar.gz(r-4.4-noble)
HQM_1.0.tgz(r-4.4-emscripten)HQM_1.0.tgz(r-4.3-emscripten)
HQM.pdf |HQM.html✨
HQM/json (API)

# Install 'HQM' in R:

install.packages('HQM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

pbc2 - Mayo Clinic Primary Biliary Cirrhosis Data

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.60 score 314 downloads 33 exports 106 dependencies

Last updated 19 days agofrom:3d200be656. Checks:2 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Feb 01 2025
R-4.5-linux	OK	Feb 01 2025

Exports:auc.hqm b_selection b_selection_prep_g bs.hqm Conf_bands dataset_split dij Epan g_xt get_alpha get_h_x get_h_xll h_xt h_xt_vec h_xtll K_b K_b_mat lin_interpolate llK_b make_N make_Ni make_sf make_Y make_Yi prep_boot prep_cv Q1 R_K sn.0 sn.1 sn.2 to_id xK_b

Dependencies:backports base64enc bslib cachem checkmate cli cluster cmprsk codetools colorspace data.table diagram digest doParallel evaluate fansi farver fastmap fontawesome foreach foreign Formula fs future future.apply ggplot2 globals glue gridExtra gtable highr Hmisc htmlTable htmltools htmlwidgets isoband iterators JM jquerylib jsonlite KernSmooth knitr labeling lattice lava lifecycle listenv magrittr MASS Matrix MatrixModels memoise mets mgcv mime multcomp munsell mvtnorm nlme nnet numDeriv parallelly pec pillar pkgconfig plotrix polspline prodlim progressr Publish quantreg R6 ranger rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen riskRegression rlang rmarkdown rms rpart rstudioapi sandwich sass scales shape SparseM SQUAREM stringi stringr survival TH.data tibble timereg timeROC tinytex utf8 vctrs viridis viridisLite withr xfun yaml zoo

Help page	Topics
AUC for the High Quality Marker estimator	auc.hqm
Cross validation bandwidth selection	b_selection
Preparations for bandwidth selection	b_selection_prep_g
Brier score for the High Quality Marker estimator	bs.hqm
Confidence bands	Conf_bands
Split dataset for K-fold cross validation	dataset_split
D matrix entries, used for the implementation of the local linear kernel	dij
Epanechnikov kernel	Epan
Computation of a key component for wild bootstrap	g_xt
Marker-only hazard rate	get_alpha
Local constant future conditional hazard rate estimator	get_h_x
Local linear future conditional hazard rate estimator	get_h_xll
Local constant future conditional hazard rate estimation at a single time point	h_xt
Hqm estimator on the marker grid	h_xt_vec
Local linear future conditional hazard rate estimation at a single time point	h_xtll
Classical (unmodified) kernel and related functionals	K_b K_b_mat xK_b
Linear interpolation	lin_interpolate
Local linear kernel	llK_b
Local linear weight functions	sn.0 sn.1 sn.2
Occurance and Exposure on grids	make_N make_Ni make_Y make_Yi
Survival function from a hazard	make_sf
Mayo Clinic Primary Biliary Cirrhosis Data	pbc2 pbc2.id
Precomputation for wild bootstrap	prep_boot
Prepare for Cross validation bandwidth selection	prep_cv
Bandwidth selection score Q1	Q1
Bandwidth selection score R	R_K
Event data frame	to_id

Package: HQM 1.0

HQM: Superefficient Estimation of Future Conditional Hazards Based on Marker Information

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)