Outlier Detection and Influence Diagnostics for Meta-Analysis
A R package for implementing outlier detection and influence diagnostics for meta-analysis. Bootstrap distributions of the influence statistics are calculated, and the thresholds to determine influential outliers are provided explicitly.

Write Reusable, Composable and Modular R Code
c(
person(
'Konrad', 'Rudolph',
email = 'konrad.rudolph@gmail.com',
role = c('aut', 'cre'),
comment = c(ORCID = '0000-0002-9866-7051')
),
person(
'Michael', 'Schubert',
email = 'mschu.dev@gmail.com',
role = 'ctb',
comment = c(ORCID = '0000-0002-6862-5221')
)
)
A modern module system for R. Organise code into hierarchical,
composable, reusable modules, and use it effortlessly across projects via a
flexible, declarative dependency loading syntax.
devtools, knitr, rmarkdown, R6, rlang, roxygen2 (>= 7.0.2),
shiny, testthat (>= 2.0.0)
Konrad Rudolph [aut, cre] (<https://orcid.org/0000-0002-9866-7051>),
Michael Schubert [ctb] (<https://orcid.org/0000-0002-6862-5221>)

devtools, knitr, rmarkdown, R6, rlang, roxygen2 (>= 7.0.2),
shiny, testthat (>= 3.0.3)

Response Transformations for Random Effect and Variance
Component Models
Response transformations for overdispersed generalized linear
models and variance component models using nonparametric profile maximum
likelihood estimation. The main function is optim.boxcox().

Box-Cox-Type Transformations for Linear and Logistic Models with
Random Effects
Box-Cox-type transformations for linear and logistic models
with random effects using non-parametric profile maximum
likelihood estimation. The main functions are optim.boxcox()
and boxcoxtype().

Box-Cox-type transformations for linear and logistic models
with random effects using non-parametric profile maximum
likelihood estimation. The main functions are optim.boxcox() for
linear models with random effects and boxcoxtype() for logistic models
with random effects.

c(
person(given="Thomas", family="Ruf", email= "Thomas.P.Ruf@me.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-9235-7079")))
Noise filter based on determining the proportion of neighboring points. A false point will be rejected if it has only few neighbors, but accepted if the proportion of neighbors in a rectangular frame is high. The size of the rectangular frame as well as the cut-off value, i.e. of a minimum proportion of neighbor-points, may be supplied or can be calculated automatically. Originally designed for the cleaning of heart rates, but suitable for filtering any slowly-changing physiological variable.For more information see Signer (2010)<doi: 10.1111/j.2041-210X.2009.00010.x>.