Fit Generalized Odds Rate Hazards Model with Interval Censored
Data
Generalized Odds Rate Hazards (GORH) model is a flexible model of fitting survival data, including the Proportional Hazards (PH) model and the Proportional Odds (PO) Model as special cases. This package fit the GORH model with interval censored data.

Data Analysis for ODS and Case-Cohort Designs with
Interval-Censoring
Shannon T. Holloway, Qingning Zhou, Jianwen Cai, Haibo Zhou
Sieve semiparametric likelihood methods for analyzing
interval-censored failure time data from an outcome-dependent sampling (ODS)
design and from a case-cohort design.
Zhou, Q., Cai, J., and Zhou, H. (2018) <doi:10.1111/biom.12744>;
Zhou, Q., Zhou, H., and Cai, J. (2017)  <doi:10.1093/biomet/asw067>.
'myOptim.R' 'minObj.R' 'baseInfo.R' 'CaseCohort_Obj.R'
'CaseCohort_gr.R' 'CaseCohort_fn.R' 'class_ICODS.R'
'CaseCohort_class.R' 'bernstein.R' 'CaseCohortIC.R'
'CaseCohort_data.R' 'ODSDesign_Obj.R' 'ODSDesign_class.R'
'ODSDesignIC.R' 'ODSDesign_data.R' 'ODSDesign_fn.R'
'ODSDesign_gr.R' 'testInputData.R'

Provides Easy Access to Complex Systems Datasets
c(
person("Raoul", "Wadhwa", email = "raoulwadhwa@gmail.com", role = c("aut", "cre")),
person("Jacob", "Scott", role = "aut"))
Provides easy-to-use and easy-to-access datasets from the
Index of COmplex Networks (ICON) database available on the University
of Colorado website. All datasets can be loaded with a single function
call and new datasets are being slowly added from ICON at
<https://icon.colorado.edu>. Specific ICON datasets of interest can
be requested at the BugReports URL <https://github.com/rrrlw/ICON/issues>.
Raoul Wadhwa [aut, cre],
Jacob Scott [aut]

c(person(given = "Raoul",
family = "Wadhwa",
role = c("aut", "cre"),
email = "raoulwadhwa@gmail.com",
comment = c(ORCID = "0000-0003-0503-9580")),
person(given = "Jacob",
family = "Scott",
role = "aut",
comment = c(ORCID = "0000-0003-2971-7673")))

Simultaneous Confidence Intervals for Ranks
Algorithms to construct confidence intervals for the ranks of centers mu_1,...,mu_n based on an independent Gaussian sample using multiple testing techniques.