knitr, DiagrammeR(>= 1.0.6.1), rmarkdown, validate, unrepx

Functions for Transforming Categorical Variables
person("Benjamin", "Rich", role=c("aut", "cre", "cph"), email="mail@benjaminrich.net")
Easily create functions to map between different sets of values,
such as for re-labelling categorical variables.

Create simple maps; add sub-plots like pie plots to a map
or any other plot; format, plot and export gridded data. The
package was developed for displaying fisheries data but most
functions can be used for more generic data visualisation.

Create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.

c(person(given = "Marcelo",
family = "Mollinari",
role = c("aut", "cre"),
email = "mmollin@ncsu.edu",
comment = c(ORCID="0000-0002-7001-8498")),
person(given = "Gabriel",
family = "Gesteira",
role = "aut",
email = "gabrielgesteira@usp.br",
comment = c(ORCID="0000-0002-4106-7346")),
person(given = "Guilhereme",
family = "Pereira",
role = "ctb",
comment = c(ORCID="0000-0002-7106-8630")),
person(given = "Augusto",
family = "Garcia",
role = "ctb",
comment = c(ORCID="0000-0003-0634-3277")),
person(given = "Zhao-Bang",
family = "Zeng",
role = "ctb",
comment = c(ORCID="0000-0002-3115-1149")),
person(given = "Katharine",
family = "Preedy",
role = c("ctb", "cph"),
comment = "MDS ordering algorithm"),
person(given = "Robert",
family = "Gentleman",
role = "cph",
comment = "C code for MLE optimization in src/pairwise_estimation.cpp"),
person(given = "Ross",
family = "Ihaka",
role = "cph",
comment = "C code for MLE optimization in src/pairwise_estimation.cpp"),
person(given = "R Foundation",
role = "cph",
comment = "C code for MLE optimization in src/pairwise_estimation.cpp"),
person(given = "R-core",
role = "cph",
comment = "C code for MLE optimization in src/pairwise_estimation.cpp"))
Construction of genetic maps in autopolyploid full-sib populations.
Uses pairwise recombination fraction estimation as the first
source of information to sequentially position allelic variants
in specific homologs. For situations where pairwise analysis has
limited power, the algorithm relies on the multilocus likelihood
obtained through a hidden Markov model (HMM). For more detail,
please see  Mollinari and Garcia (2019) <doi:10.1534/g3.119.400378>
and Mollinari et al. (2020) <doi:10.1534/g3.119.400620>.
Rcpp (>= 0.12.6), RCurl, plotly, fields, ggpubr, ggsci,
rstudioapi, plot3D, memuse, dplyr, crayon, cli, magrittr,
reshape2, ggplot2, smacof, princurve, dendextend, vcfR
testthat, RColorBrewer, updog, covr, polymapR
Marcelo Mollinari [aut, cre] (<https://orcid.org/0000-0002-7001-8498>),
Gabriel Gesteira [aut] (<https://orcid.org/0000-0002-4106-7346>),
Guilhereme Pereira [ctb] (<https://orcid.org/0000-0002-7106-8630>),
Augusto Garcia [ctb] (<https://orcid.org/0000-0003-0634-3277>),
Zhao-Bang Zeng [ctb] (<https://orcid.org/0000-0002-3115-1149>),
Katharine Preedy [ctb, cph] (MDS ordering algorithm),
Robert Gentleman [cph] (C code for MLE optimization in
src/pairwise_estimation.cpp),
Ross Ihaka [cph] (C code for MLE optimization in
src/pairwise_estimation.cpp),
R Foundation [cph] (C code for MLE optimization in
src/pairwise_estimation.cpp),
R-core [cph] (C code for MLE optimization in
src/pairwise_estimation.cpp)