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

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>.

Data Analysis using Bootstrap-Coupled Estimation
c(
person("Joses W.", "Ho",
email = "joseshowh@gmail.com", role = c("cre", "aut")),
person("Tayfun", "Tumkaya",
role = c("aut"))
)
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
dichotomy engendered by P values.
An estimation plot has two key features:
1. It presents all datapoints as a swarmplot, which orders each point to
display the underlying distribution.
2. It presents the effect size as a bootstrap 95% confidence interval on a
separate but aligned axes.
Estimation plots are introduced in Ho et al (2018) <doi:10.1101/377978>.
cowplot, dplyr, ggplot2 (>= 3.0), grid, forcats, ggforce,
ggbeeswarm, rlang, simpleboot, stringr, tibble, tidyr
knitr, rmarkdown, tufte, testthat, vdiffr
Joses W. Ho [cre, aut],
Tayfun Tumkaya [aut]

cowplot, dplyr, ggplot2 (>= 3.0), forcats, ggforce,
ggbeeswarm, rlang, simpleboot, stringr, tibble, tidyr

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
dichotomy engendered by P values.
An estimation plot has two key features:
1. It presents all datapoints as a swarmplot, which orders each point to
display the underlying distribution.
2. It presents the effect size as a bootstrap 95% confidence interval on a
separate but aligned axes.
Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105.
<doi:10.1038/s41592-019-0470-3>.
The free-to-view PDF is located at <https://rdcu.be/bHhJ4>.