Title: Effect Size Computation for Meta Analysis
Description: Implementation of the web-based 'Practical Meta-Analysis Effect Size
Calculator' from David B. Wilson in R. Based on the input, the effect size
can be returned as standardized mean difference, Hedges' g, correlation
coefficient r or Fisher's transformation z, odds ratio or log odds effect size.
Packaged: 2016-07-08 16:02:41 UTC; Standardbenutzer

Packaged: 2016-09-07 11:25:19 UTC; Standardbenutzer

Calculator' from David B. Wilson (<http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php>)
in R. Based on the input, the effect size can be returned as standardized mean
difference, Hedges' g, correlation coefficient r or Fisher's transformation z,
Imports: dplyr, purrr, readr, sjmisc (>= 2.3.1), tibble, utils

Imports: dplyr, purrr, readr, sjmisc (>= 2.5.0), tibble, utils

Imports: dplyr, purrr, readr, sjmisc (>= 2.6.1), tibble, utils

difference, Cohen's f, Hedges' g, Pearson's r or Fisher's
transformation z, odds ratio or log odds, or eta squared effect size.

Authors@R: person("Daniel", "Lüdecke", role = c("aut", "cre"), email = "d.luedecke@uke.de", comment = c(ORCID = "0000-0002-8895-3206"))
Author: Daniel Lüdecke [aut, cre] (<https://orcid.org/0000-0002-8895-3206>)

Package: escalation
Type: Package
Title: Modular Approach to Dose Finding Clinical Trials
Version: 0.1.1
Date: 2020-02-29
Authors@R: person("Kristian", "Brock", email = "kristian.brock@gmail.com",
role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3921-0166"))
Maintainer: Kristian Brock <kristian.brock@gmail.com>
Description: Methods for working with dose-finding clinical trials. We start by
providing a common interface to various dose-finding methodologies like the
continual reassessment method (CRM) by O'Quigley et al. (1990)
<doi:10.2307/2531628>, the Bayesian optimal interval design (BOIN) by Liu &
Yuan (2015) <doi:10.1111/rssc.12089>, and the 3+3 described by Korn et al.
(1994) <doi:10.1002/sim.4780131802>. We then add optional embellishments to
provide extra desirable behaviour, like avoiding skipping doses, stopping
after n patients have been treated at the recommended dose, or demanding
that n patients are treated before stopping is allowed. By daisy-chaining
together these embellishments using the pipe operator from 'magrittr', it is
simple to tailor the behaviour of dose-finding designs so that they do what
you want. Furthermore, using this flexible interface for creating
dose-finding designs, it is simple to run simulations or calculate
dose-pathways for future cohorts of patients.
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Depends: magrittr
Imports: dplyr, tidyr (>= 1.0), tidyselect, stringr, purrr, tibble,
gtools, dfcrm, BOIN
RoxygenNote: 7.0.2
Suggests: testthat, knitr, rmarkdown, covr
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-02-29 15:10:05 UTC; brockk
Author: Kristian Brock [aut, cre] (<https://orcid.org/0000-0002-3921-0166>)
Repository: CRAN
Date/Publication: 2020-03-08 17:10:02 UTC