The primary goal of phase I clinical trials is to find the maximum tolerated dose (MTD). To reach this objective, we introduce a new design for phase I clinical trials, the posterior predictive (PoP) design. The PoP design is an innovative model-assisted design that is as simply as the conventional algorithmic designs as its decision rules can be pre-tabulated prior to the onset of trial, but is of more flexibility of selecting diverse target toxicity rates and cohort sizes. The PoP design has desirable properties, such as coherence and consistency. Moreover, the PoP design provides better empirical performance than the BOIN and Keyboard design with respect to high average probabilities of choosing the MTD and slightly lower risk of treating patients at subtherapeutic or overly toxic doses.

c(person(given = "Chenqi",
family = "Fu",
role = "aut"),
person(given = "Xinying",
family = "Fang",
role = c("aut","cre"),
email = "fxy950225@gmail.com"),
person(given = "Shouhao",
family = "Zhou",
role = "aut"))
Chenqi Fu [aut],
Xinying Fang [aut, cre],
Shouhao Zhou [aut]

Valid Post-Selection Inference for Linear LS Regression
In linear LS regression, calculate for a given design matrix
the multiplier K of coefficient standard errors such that the
confidence intervals [b - K*SE(b), b + K*SE(b)] have a
guaranteed coverage probability for all coefficient estimates
b in any submodels after performing arbitrary model selection.

c(person("Andreas", "Buja", role = "aut"),
person("Kai", "Zhang", role = "aut"),
person("Wan", "Zhang", role = "cre",
email = "wanz63@live.unc.edu"))
Andreas Buja [aut],
Kai Zhang [aut],
Wan Zhang [cre]

PogromcyDanych / DataCrunchers is the Masive Online Open Course
that Brings R and Statistics to the People
The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment.