Package: HQM 0.1.0
Alex Isakson
HQM: Superefficient Estimation of Future Conditional Hazards Based on Marker Information
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.
Authors:
HQM_0.1.0.tar.gz
HQM_0.1.0.tar.gz(r-4.5-noble)HQM_0.1.0.tar.gz(r-4.4-noble)
HQM_0.1.0.tgz(r-4.4-emscripten)HQM_0.1.0.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')) |
- 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.
Last updated 2 years agofrom:c8c0405289. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 17 2024 |
| R-4.5-linux | OK | Sep 17 2024 |
Exports:b_selectionb_selection_prep_gConf_bandsdataset_splitEpang_xtget_alphaget_h_xh_xth_xt_vecK_bK_b_matlin_interpolatemake_Nmake_Nimake_sfmake_Ymake_Yiprep_bootprep_cvQ1R_Kto_id
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Cross validation bandwidth selection | b_selection |
| Preparations for bandwidth selection | b_selection_prep_g |
| Confidence bands | Conf_bands |
| Split dataset for K-fold cross validation | dataset_split |
| Computation of a key component for wild bootstrap | g_xt |
| Marker-only hazard rate | get_alpha |
| Future conditional hazard rate for all time values | get_h_x |
| Future conditional hazard rate | h_xt |
| Hqm estimator on the marker grid | h_xt_vec |
| Epanechnikov kernel and pdf kernel estimate | Epan K_b K_b_mat |
| Linear interpolation | lin_interpolate |
| 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 |