Title: Multi-Way Spherical Clustering via Degree-Corrected Tensor Block
Authors@R: c(person(given = "Jiaxin",family = "Hu",role = c("aut", "cre","cph"),email = "jhu267@wisc.edu"), person(given = "Miaoyan",family = "Wang",role = c("aut", "cph"),email = "miaoyan.wang@wisc.edu"))
Description: Implement weighted higher-order initialization and angle-based iteration for multi-way spherical clustering under degree-corrected tensor block model.

Title: Damped Anderson Acceleration with Epsilon Monotonicity for
Authors@R: c(person("Nicholas", "Henderson", email = "nhender5@jhmi.edu", role = "cre"), person("Ravi", "Varadhan", email = "ravi.varadhan@jhu.edu", role = "aut"))
Description: Implements the DAAREM method for accelerating the convergence of slow, monotone sequences from smooth, fixed-point iterations such as the EM algorithm. For further details about the DAAREM method. see Henderson, N.C. and Varadhan, R. (2018) <arXiv:1803.06673>.

Authors@R: c(person("Nicholas", "Henderson", email = "nhender5@jhmi.edu", role = c("cre", "aut")), person("Ravi", "Varadhan", email = "ravi.varadhan@jhu.edu", role = "aut"))

Authors@R: c(person("Nicholas", "Henderson", email = "nchender@umich.edu", role = c("cre", "aut")), person("Ravi", "Varadhan", email = "ravi.varadhan@jhu.edu", role = "aut"))

Description: Implements the DAAREM method for accelerating the convergence of slow, monotone sequences from smooth, fixed-point iterations such as the EM algorithm. For further details about the DAAREM method, see Henderson, N.C. and Varadhan, R. (2019) <doi:10.1080/10618600.2019.1594835>.

Title: Data Analysis using Bootstrap-Coupled Estimation
email = "joseshowh@gmail.com", role = c("cre", "aut")),
Maintainer: Joses W. Ho <joseshowh@gmail.com>
Description: Data Analysis using Bootstrap-Coupled ESTimation.
Estimation statistics is a simple framework that avoids the pitfalls of
significance testing. It uses familiar statistical concepts: means,
mean differences, and error bars. More importantly, it focuses on the
effect size of one's experiment/intervention, as opposed to a false
An estimation plot has two key features:
1. It presents all datapoints as a swarmplot, which orders each point to
2. It presents the effect size as a bootstrap 95% confidence interval on a
Estimation plots are introduced in Ho et al (2018) <doi:10.1101/377978>.
Imports: cowplot, dplyr, ggplot2 (>= 3.0), grid, forcats, ggforce,
ggbeeswarm, rlang, simpleboot, stringr, tibble, tidyr
Suggests: knitr, rmarkdown, tufte, testthat, vdiffr
Packaged: 2018-11-13 14:19:53 UTC; joseshowh