Title: Hierarchical Community Detection by Recursive Partitioning
Author: Tianxi Li, Lihua Lei, Sharmodeep Bhattacharyya, Purnamrita Sarkar, Peter Bickel, and Elizaveta Levina
Description: Hierarchical community detection on networks by a recursive spectral partitioning strategy, which is shown to be effective and efficient in Li, Lei, Bhattacharyya, Sarkar, Bickel, and Levina (2018) <arXiv:1810.01509>. The package also includes a data generating function for a binary tree stochastic block model, a special case of stochastic block model that admits hierarchy between communities.
Imports: Matrix, stats, methods, randnet, RSpectra, irlba, data.tree,

Title: Causal Discovery on Discrete Data using Hidden Compact
Description: This code provides a method to fit the hidden compact representation model as well as to identify the causal direction on discrete data.
We implement an effective solution to recover the above hidden compact representation under the likelihood framework.
Please see the Causal Discovery on Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) <https://nips.cc/Conferences/2018/Schedule?showEvent=11274> for a description of some of our methods.
Imports: data.table (>= 1.10.4), methods

Title: Causal Discovery from Discrete Data using Hidden Compact
Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) <https://nips.cc/Conferences/2018/Schedule?showEvent=11274> for a description of some of our methods.

Title: Calculates Significance Criteria and Power for a Single Arm
Maintainer: David A. Schoenfeld <dschoenfeld@mgh.harvard.edu>
Description: Given a database of previous treatment/placebo estimates, their standard errors and sample sizes,
the program calculates a significance criteria and power estimate that takes into account the among

Title: Group Sequential Design for Historical Control Trial with
Imports: Rdpack, diversitree, mvtnorm, flexsurv, stats, survival
Authors@R: c(person("Tushar", "Patni", role = c("aut", "cre"),email = "patni006@umn.edu"),person("Yimei", "Li", role = "aut"),person("Jianrong", "Wu", role = "aut"),person("Arzu", "Onar-Thomas", role = "aut"))
Description: It provides functions to design historical controlled trials with survival outcome by group sequential method. The options for interim look boundaries are efficacy only, efficacy & futility or futility only. It also provides the function to monitor the trial for any unplanned look. The package is based on Jianrong Wu, Xiaoping Xiong (2016) <doi:10.1002/pst.1756> and Jianrong Wu, Yimei Li (2020) <doi:10.1080/10543406.2019.1684305>.