c(person("Tarn", "Duong", role=c("aut","cre"), email="tarn.duong@gmail.com", comment=c(ORCID="0000-0002-1198-3482")))
dplyr, FNN, ggplot2 (>= 3.0.0), isoband, ks, mapsf, sf
colorspace, ggquiver, ggspatial, ggthemes, knitr, MASS,
rmarkdown
Extensions of the kernel smoothing functions from the 'ks' package for compatibility with the tidyverse and geospatial ecosystems.

dplyr, ggplot2 (>= 3.0.0), isoband, ks, mapsf, sf
Extensions of the kernel smoothing functions from the 'ks' package for compatibility with the tidyverse and geospatial ecosystems <doi:10.48550/arXiv.2203.01686>.

empirical likelihood ratio tests for means

A 'shiny' Application for Exploring Laboratory Data
Silke Janitza, Steffen Jeske, Madhurima Majumder, Franco Mendolia, Hermann Kulmann
A novel concept for generating knowledge and gaining insights into laboratory data. You will be able to efficiently and easily explore your laboratory data from different perspectives.
shiny, V8, dplyr, purrr, rlang, tidyr, forcats, RColorBrewer,
bsplus, dendextend, haven, plotly, reshape2, shape, shinyBS,
shinyWidgets, shinycssloaders, shinydashboard, shinyjs, gclus

c(person("Silke", "Janitza", role = "aut"),
person("Steffen", "Jeske", email = "steffen.jeske.ext@bayer.com", role = c("aut","cre")),
person("Madhurima", "Majumder", role = "aut"),
person("Franco", "Mendolia", role = "aut"),
person("Hermann", "Kulmann", role = "aut"))
Silke Janitza [aut],
Steffen Jeske [aut, cre],
Madhurima Majumder [aut],
Franco Mendolia [aut],
Hermann Kulmann [aut]
shiny, dplyr, purrr, rlang, tidyr, forcats, RColorBrewer,
bsplus, dendextend, haven, reshape2, shape, shinyWidgets,
shinydashboard

Elastic Analysis of Sparse, Dense and Irregular Curves
Provides functions to align curves and to compute mean curves based on the
elastic distance defined in the square-root-velocity framework. For more details on
this framework see Srivastava and Klassen (2016, <doi:10.1007/978-1-4939-4020-2>).

Provides functions to align curves and to compute mean curves based on the
elastic distance defined in the square-root-velocity framework. For more details on
this framework see Srivastava and Klassen (2016, <doi:10.1007/978-1-4939-4020-2>).
For more theoretical details on our methods and algorithms see
Steyer et al. (2021, <arXiv:2104.11039>).