One-Arm Clinical Trial Designs for Time-to-Event Endpoint
c(person(given = "Heng",
family = "Zhou",
role = c("aut", "cre"),
email = "heng.zhou@merck.com"),
person(given = "Keaven",
family = "Anderson",
role = "aut"),
person(given = "Linda",
family = "Sun",
role = "aut"),
person(given = "Meihua",
family = "Wang",
role = "aut"))
Get operating characteristics of one-arm clinical trial designs for time-to-event endpoint through simulation and perform analysis with time-to-event data.
Heng Zhou [aut, cre],
Keaven Anderson [aut],
Linda Sun [aut],
Meihua Wang [aut]

Prediction of condition-specific transcription factor
interactions
Predictions of condition-specific TF interactions using
gene activity data, such as mRNA expression measurements and a
TF-target graph.

c(person("Sebastian", "Dümcke", role=c("aut","cre")
,email="duemcke@genzentrum.lmu.de"),
person("Achim","Tresch",role="ths"))
Sebastian Dümcke [aut, cre], Achim Tresch [ths]

Machine Learning Classification Algorithm with Enhancements
Implements the One Rule (OneR) Machine Learning classification algorithm with enhancements for sophisticated handling of missing values and numeric data together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics.

One Rule Machine Learning Classification Algorithm with
Enhancements
Implements the One Rule (OneR) Machine Learning classification algorithm with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics.

Implements the One Rule (OneR) Machine Learning classification algorithm (Holte, R.C. (1993) <doi:10.1023/A:1022631118932>) with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics.