Description: Software of 'esDesign' is developed to implement the adaptive enrichment designs with sample size re-estimation. In details, three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR), AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package also contains several widely used adaptive designs, such as the Marker Sequential Test (MaST) design proposed Freidlin et al. (2014) <doi:10.1177/1740774513503739>, the adaptive enrichment designs without early stopping (AED or ES), the sample size re-estimation procedure (SSR) based on the conditional power proposed by Proschan and Hunsberger (1995), and some useful functions. In details, we can calculate the futility and/or efficacy stopping boundaries, the sample size required, calibrate the value of the threshold of the difference between subgroup-specific test statistics, conduct the simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR.

Description: Software of 'esDesign' is developed to implement the adaptive
enrichment designs with sample size re-estimation presented in
three-proposed trial designs are provided, including the AED1-SSR (or ES1-SSR),
AED2-SSR (or ES2-SSR) and AED3-SSR (or ES3-SSR). In addition, this package
also contains several widely used adaptive designs, such as the Marker
Sequential Test (MaST) design proposed Freidlin et al.
without early stopping (AED or ES), the sample size re-estimation
procedure (SSR) based on the conditional power proposed by
Proschan and Hunsberger (1995), and some useful functions.
In details, we can calculate the futility and/or efficacy stopping
boundaries, the sample size required, calibrate the value of the threshold
of the difference between subgroup-specific test statistics, conduct the
simulation studies in AED, SSR, AED1-SSR, AED2-SSR and AED3-SSR.

Title: Estimate Number of Latent Factors and Factor Matrix for Factor
Authors@R: c(person(c("Art", "B."), "Owen", role = c("aut")), person("Jingshu", "Wang", email = "wangjingshususan@gmail.com", role = c("aut", "cre")) )
Description: These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (http://arxiv.org/abs/1503.03515).