R interface to the CSDP semidefinite programming library.
Installs version 6.1.1 of CSDP from the COIN-OR website if
required. An existing installation of CSDP may be used by
passing the proper configure arguments to the installation
command. See the INSTALL file for further details.

R interface to the CSDP semidefinite programming library. Installs version 6.1.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. See the INSTALL file for further details.

R Interface to the CSDP Semidefinite Programming Library

c(
person("Hector", "Corrada Bravo", email="hcorrada@gmail.com", role=c("aut", "cre")),
person("Brian", "Borchers", role="aut"),
person("Don", "van den Bergh", rol="ctb")
)
Hector Corrada Bravo [aut, cre],
Brian Borchers [aut],
Don van den Bergh [ctb]

c(
person("Hector", "Corrada Bravo", email="hcorrada@gmail.com", role=c("aut", "cre")),
person("Brian", "Borchers", role="aut"),
person("Don", "van den Bergh", role="ctb")
)

Styling of Graphics using Cascading Style Sheets
person("Tomasz", "Konopka", , "tokonopka@gmail.com", role = c("aut", "cre"))
Provides a means to style plots through cascading style sheets.
This separates the aesthetics from the data crunching in plots and charts.

methods, graphics, grDevices, stats, utils

Concentration-Response Data Analysis using Curvep
c(person("Jui-Hua", "Hsieh", email = "juihua.hsieh@gmail.com", role = c("aut", "cre")), person("Alexander", "Sedykh", email = "sunirks@gmail.com", role = c("aut")))
Provide an R interface for processing concentration-response datasets using Curvep, a response noise filtering algorithm. The algorithm was described in the publications (Sedykh A et al. (2011) <doi:10.1289/ehp.1002476> and Sedykh A (2016) <doi:10.1007/978-1-4939-6346-1_14>). Other parametric fitting approaches (e.g., Hill equation) are also adopted for ease of comparison. Also, methods for calculating the confidence interval around the activity metrics are also provided. The methods are based on the bootstrap approach to simulate the datasets (Hsieh J-H et al. <doi:10.1093/toxsci/kfy258>). The simulated datasets can be used to derive the baseline noise threshold in an assay endpoint. This threshold is critical in the toxicological studies to derive the point-of-departure (POD).
dplyr (>= 0.7), tibble, magrittr, tidyselect, boot, tidyr,
purrr, rlang, stringr, ggplot2, Rdpack, methods
Jui-Hua Hsieh [aut, cre],
Alexander Sedykh [aut]