The framework provides functions to generate ODEs of reaction
networks, parameter transformations, observation functions, residual functions,
etc. The framework follows the paradigm that derivative information should be
used for optimization whenever possible. Therefore, all major functions produce
and can handle expressions for symbolic derivatives.
MASS, rPython, deSolve, rootSolve, pander, knitr, rmarkdown

deSolve, rootSolve, ggplot2, parallel, stringr, plyr, digest,
dplyr, rlang

deSolve, rootSolve, ggplot2, parallel, stringr, plyr

deSolve, rootSolve, ggplot2, parallel, stringr, plyr, dplyr,
foreach, doParallel

The framework provides functions to generate ODEs of reaction
networks, parameter transformations, observation functions, residual functions,
etc. The framework follows the paradigm that derivative information should be
used for optimization whenever possible. Therefore, all major functions produce
and can handle expressions for symbolic derivatives. The methods used in dMod
were published in Kaschek et al, 2019, <doi:10.18637/jss.v088.i10>.
MASS, reticulate, pander, knitr, rmarkdown

Automated Analysis of Multiplex Digital PCR Data
c(person(given = "Alfonso", family = "De Falco",
role = c("aut", "cre"),
email = "alfonso.defalco@lns.etat.lu",
comment = c(ORCID = "0000-0001-9985-9920")),
person(given = "Michel", family = "Mittelbronn", role = c("ctb")),
person(given = "Christophe", family = "Olinger", role = c("ctb")),
person(given = "Daniel", family = "Stieber", role = c("ctb")),
person(given = "Laboratoire national de santé", role = c("cph")))
The automated clustering and quantification of the digital PCR
data is based on the combination of 'DBSCAN' (Hahsler et al. (2019)
<doi:10.18637/jss.v091.i01>) and 'c-means' (Bezdek et l. (1981)
<doi:10.1007/978-1-4757-0450-1>) algorithms.
The analysis is independent of multiplexing geometry, dPCR system,
and input amount.
The details about input data and parameters are available in the
vignette.
dbscan, e1071, exactci, ggplot2, ggpubr, graphics, raster,
rlist, scales, shiny, shinyjs, stats, stringr, utils
Alfonso De Falco [aut, cre] (<https://orcid.org/0000-0001-9985-9920>),
Michel Mittelbronn [ctb],
Christophe Olinger [ctb],
Daniel Stieber [ctb],
Laboratoire national de santé [cph]