c(person("Stephane", "Champely", role=c("aut")),
person("Claus", "Ekstrom", role="ctb"),
person("Peter", "Dalgaard", role="ctb"),
person("Jeffrey", "Gill", role="ctb"),
person("Stephan", "Weibelzahl", role="ctb"),
person("Helios", "De Rosario", role="cre",
email="helios.derosario@gmail.com"))
Stephane Champely [aut],
Claus Ekstrom [ctb],
Peter Dalgaard [ctb],
Jeffrey Gill [ctb],
Stephan Weibelzahl [ctb],
Helios De Rosario [cre]

c(person("Stephane", "Champely", role=c("aut")),
person("Claus", "Ekstrom", role="ctb"),
person("Peter", "Dalgaard", role="ctb"),
person("Jeffrey", "Gill", role="ctb"),
person("Stephan", "Weibelzahl", role="ctb"),
person("Aditya", "Anandkumar", role="ctb"),
person("Clay", "Ford", role="ctb"),
person("Robert", "Volcic", role="ctb"),
person("Helios", "De Rosario", role="cre",
email="helios.derosario@gmail.com"))
Stephane Champely [aut],
Claus Ekstrom [ctb],
Peter Dalgaard [ctb],
Jeffrey Gill [ctb],
Stephan Weibelzahl [ctb],
Aditya Anandkumar [ctb],
Clay Ford [ctb],
Robert Volcic [ctb],
Helios De Rosario [cre]

Power and Sample Size Analysis for One-way and Two-way ANOVA
Models
Pengcheng Lu, Junhao Liu, Devin Koestler
User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers.

Power Analyses for Common Designs (Power to the People)
Statistical power analysis for designs including t-tests, correlations,
multiple regression, ANOVA, mediation, and logistic regression. Functions accompany
Aberson (2019) <doi:10.4324/9781315171500>.

person("William", "Cha", email = "william.minseuk.cha@gmail.com",
role = c("aut", "cre"))
Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test,
which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are
unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give
biased results due to using the pooled standard deviation, unlike the Welch's t-test.