Description: Transformation of ODM files into R format, office format and vice versa. CDISC ODM format is commonly used in clinical trials. Semantic annotation (such as UMLS, SNOMED or LOINC codes) are transformed accordingly.

Description: Transformation of ODM files into R format, office format, CDA format and vice versa. CDISC ODM format is commonly used in clinical trials. Semantic annotation (such as UMLS, SNOMED or LOINC codes) are transformed accordingly.

Description: Transformation of 'ODM' (see http://www.cdisc.org/odm) files into R format, Microsoft Office format, 'CDA' format and vice versa. 'ODM' format is commonly used in clinical trials. Semantic annotation (such as 'UMLS', 'SNOMED' or 'LOINC' codes) are transformed accordingly.

Description: Transformation of 'ODM' (see <http://www.cdisc.org/odm>) files into R format, Microsoft Office format, 'CDA' format and vice versa. 'ODM' format is commonly used in clinical trials. Semantic annotation (such as 'UMLS', 'SNOMED' or 'LOINC' codes) are transformed accordingly.

Title: OD-Means: k-Means for Origin-Destination
person("Cristobal", "Heredia", role = "aut"),
person("Wilfredo", "Yushimito", role = "ths")
Description: 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

Title: Statistical Methods for Outcome-Dependent Sampling Designs
Authors@R: c(person("Yinghao", "Pan", role = c("aut", "cre"),
Description: 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>.
Imports: cubature (>= 1.4-1), survival (>= 2.42-3), utils, stats
Packaged: 2018-11-18 23:43:39 UTC; yinghaopan