c(
person("Scott", "Chamberlain",
role = c("aut", "cre"),
email = "myrmecocystus@gmail.com",
comment = c(ORCID = "0000-0003-1444-9135")))
crul (>= 0.4.0), curl, ape, taxize, phytools, phylocomr (>=
0.1.4), conditionz

crul (>= 0.4.0), curl, ape, taxize (>= 0.9.97), phytools,
phylocomr (>= 0.1.4), conditionz

Includes methods for fetching 'phylogenies' from a variety
of sources, including the 'Phylomatic' web service
(<http://phylodiversity.net/phylomatic/>), and 'Phylocom'
(<https://github.com/phylocom/phylocom/>).

Analyses of large-scale -omics datasets commonly use p-values as the indicators of statistical significance. However, considering p-value alone neglects the importance of effect size (i.e., the mean difference between groups) in determining the biological relevance of a significant difference. Here, we present a novel algorithm for computing a new statistic, the biological relevance testing (BRT) index, in the frequentist hypothesis testing framework to address this problem.

knitr, rmarkdown, reshape2, vsn, DESeq2, pasilla

Broadly Useful Convenient and Efficient R Functions
Broadly useful convenient and efficient R functions
that bring users concise and elegant R data analyses.
The package includes easy-to-use functions for
(1) basic use and analysis;
(2) multivariate computation;
(3) reliability and validity analysis;
(4) multi-factor analysis of variance (ANOVA),
simple-effect analysis, and post-hoc multiple comparison;
and (5) advanced toolbox and tidy report of statistical models.
pacman, jmv, mediation, interactions, MuMIn, performance,
texreg, cowplot, see, glue, crayon, ggplot2
rstudioapi, rio, dplyr, tidyr, stringr, forcats, data.table,
car, psych, afex, emmeans, effectsize, jtools, lavaan, lmtest,
lme4, lmerTest, nnet, MASS, GGally

Broadly useful convenient and efficient R functions
that bring users concise and elegant R data analyses.
This package includes easy-to-use functions for
(1) basic R programming
(e.g., set working directory to where the current file is,
print strings with rich formats and colors);
(2) multivariate computation
(e.g., compute scale sums/means/... with reverse scoring);
(3) reliability and factor analyses;
(4) descriptive statistics and correlation analyses;
(5) multi-factor analysis of variance (ANOVA),
simple-effect analysis, and post-hoc multiple comparison;
(6) tidy report of regression models (to Console and to Word);
(7) mediation and moderation analyses;
and (8) additional toolbox for statistics and graphics.
pacman, jmv, mediation, interactions, MuMIn, performance,
texreg, cowplot, see, glue, crayon, ggplot2, ggtext