Simulating the Molecular Evolutionary Process of Retrotransposon
Recombination
c(
person("Anindya", "Sharma", , "anindya.sharma1@gmail.com", role = c("aut", "cre")),
person("Hajk-Georg", "Drost", , "hajk-georg.drost@tuebingen.mpg.de", role = c("aut", "ctb"))
)
An optimized framework to simulate the molecular evolutionary processes of retrotransposon evolution including extrachromosomal recombination.
Rcpp (>= 0.12.17), dplyr, ggplot2, magrittr, rlang
Anindya Sharma [aut, cre],
Hajk-Georg Drost [aut, ctb]

Tools for Type S (Sign) and Type M (Magnitude) Errors
c(
person("Andrew", "Timm", email = "timmandrew1@gmail.com", role = c("cre","aut")),
person("Andrew", "Gelman", role = c("ctb", "cph")),
person("John", "Carlin", role = c("ctb", "cph"))
)
Provides tools for working with Type S (Sign) and
Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000)
<doi.org/10.1007/s001800000040> and
Gelman & Carlin (2014) <doi.org/10.1177/1745691614551642>.
In addition to simply calculating the probability of
Type S/M error, the package includes functions for calculating these errors
across a variety of effect sizes for comparison, and recommended sample size
given "tolerances" for Type S/M errors. To improve the speed of these
calculations, closed forms solutions for the probability of a Type S/M error
from Lu, Qiu, and Deng (2018) <doi.org/10.1111/bmsp.12132>
are implemented. As of 1.0.0, this includes
support only for simple research designs. See the
package vignette for a fuller exposition on how Type S/M errors arise in
research, and how to analyze them using the type of design analysis proposed
in the above papers.
ggplot2, knitr, rmarkdown, gridExtra, testthat
Andrew Timm [cre, aut],
Andrew Gelman [ctb, cph],
John Carlin [ctb, cph]

c(person(given = "Daniel",
family = "Antal",
role = c("aut", "cre"),
email = "daniel.antal@ceemid.eu",
comment = c(ORCID = "0000-0001-7513-6760")),
person(given = "Marta",
family = "Kolczynska",
role = c("ctb"),
email = "mkolczynska@gmail.com",
comment = c(ORCID = "0000-0003-4981-0437"))
)
Assist in reproducible retrospective (ex-post) harmonization of data,
particularly individual level survey data, by providing tools for organizing metadata,
standardizing the coding of variables, and variable names and value labels,
including missing values, and documenting the data transformations,
with the help of comprehensive s3 classes.
vctrs, haven, dplyr (>= 1.0.0), magrittr, stats, tibble,
labelled, methods, rlang, fs, assertthat, tidyselect, pillar,
snakecase, purrr, tidyr, here