c(
person("Sebastian","Moreno",role = c("aut", "cre"),
email = "sebastian.moreno.araya@gmail.com"),
person("Cristobal", "Heredia", role = "aut"),
person("Wilfredo", "Yushimito", role = "ths")
)
OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs.
In the first layer of the hierarchy, the clusters are separated automatically based on the variation
of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy
corresponds to the sub clustering process of small clusters based on the distance between the origin
and destination of each cluster.
Sebastian Moreno [aut, cre],
Cristobal Heredia [aut],
Wilfredo Yushimito [ths]

Statistical Methods for Outcome-Dependent Sampling Designs
c(person("Yinghao", "Pan", role = c("aut", "cre"),
email = "ypan8@uncc.edu"),
person("Haibo", "Zhou", role = "aut",
email = "zhou@bios.unc.edu"),
person("Mark", "Weaver", role = "aut"),
person("Guoyou", "Qin", role = "aut"),
person("Jianwen", "Cai", role = "aut"))
Yinghao Pan [aut, cre],
Haibo Zhou [aut],
Mark Weaver [aut],
Guoyou Qin [aut],
Jianwen Cai [aut]
Outcome-dependent sampling (ODS) schemes are cost-effective ways to enhance study efficiency.
In ODS designs, one observes the exposure/covariates with a probability that depends on the outcome
variable. Popular ODS designs include case-control for binary outcome, case-cohort for time-to-event
outcome, and continuous outcome ODS design (Zhou et al. 2002) <doi: 10.1111/j.0006-341X.2002.00413.x>.
Because ODS data has biased sampling nature, standard statistical analysis such as linear regression
will lead to biases estimates of the population parameters. This package implements four statistical
methods related to ODS designs: (1) An empirical likelihood method analyzing the primary continuous
outcome with respect to exposure variables in continuous ODS design (Zhou et al., 2002). (2) A partial
linear model analyzing the primary outcome in continuous ODS design (Zhou, Qin and Longnecker, 2011)
<doi: 10.1111/j.1541-0420.2010.01500.x>. (3) Analyze a secondary outcome in continuous ODS design
(Pan et al. 2018) <doi: 10.1002/sim.7672>. (4) An estimated likelihood method analyzing a secondary
outcome in case-cohort data (Pan et al. 2017) <doi: 10.1111/biom.12838>.
cubature (>= 1.4-1), survival (>= 2.42-3), utils, stats