Title: Sample Size and Power for Association Studies Involving
Description: Calculate Sample Size and Power for
Author: Aurora Baluja [aut, cre] (<https://orcid.org/0000-0002-5204-0771>)

Authors@R: c(person("Juhui", "WANG", role = c("aut", "cre"),
comment = "Applications and Statistical Methods"),
person("Robert", "FAIVRE", role = "ctb",
person("Hervé", "RICHARD", role = "ctb",
Author: Juhui WANG [aut, cre] (Software and Engineering), Hervé MONOD [aut] (Applications and Statistical Methods), Robert FAIVRE [ctb] (Applications and Statistical Methods), Hervé RICHARD [ctb] (Software and Engineering)
Description: MTK (Mexico ToolKit) is a generic platform for the sensitivity and uncertainty analysis of complex models. It provides functions and facilities for experimental design, model simulation, sensitivity and uncertainty analysis, methods integration and data reporting, etc.
Depends: R (>= 2.15.0), base, stringr, graphics, methods, XML,

Title: Unsupervised Multi-Task and Transfer Learning on Gaussian
Authors@R: c(person("Ye", "Tian", role = c("aut", "cre"), email = "ye.t@columbia.edu"), person("Haolei", "Weng", role = "aut", email = "wenghaol@msu.edu"), person("Yang", "Feng", role = "aut", email = "yang.feng@nyu.edu"))
Description: Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the Expectation-Maximization (EM) algorithm that not only can effectively utilize unknown similarity between related tasks but is also robust against a fraction of outlier tasks from arbitrary sources. The proposed procedure is shown to achieve minimax optimal rate of convergence for both parameter estimation error and the excess mis-clustering error, in a wide range of regimes. Moreover, we generalize our approach to tackle the problem of transfer learning for GMMs, where similar theoretical results are derived. Finally, we demonstrate the effectiveness of our methods through simulations and a real data analysis. To the best of our knowledge, this is the first work studying multi-task and transfer learning on GMMs with theoretical guarantees. This package implements the algorithms proposed in Tian, Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>.
Imports: doParallel, foreach, caret, mclust, stats