Title: Ready to Print Monthly and Yearly Calendars Made with 'ggplot2'
person("José Carlos", "Soage González", email = "jsoage@uvigo.es", role = c("aut", "cre")),
person("Natalia", "Pérez Veiga", email = "naperez@uvigo.es", role = "aut")
Description: Contains the function calendR() for creating fully customizable monthly and yearly calendars (colors, fonts, formats, ...) and even heatmap calendars. In addition, it allows saving the calendars in ready to print A4 format PDF files.
Author: José Carlos Soage González [aut, cre],

Imports: ggplot2, dplyr, forcats, suncalc, ggimage, gggibbous

Title: 'calendR' Fork with Additional Features (Backwards Compatible)
Description: Fork of 'calendR' R package to generate ready to print
calendars with 'ggplot2' (see <https://r-coder.com/calendar-plot-r/>)
with additional features (backwards compatible).
'calendRio' provides a 'calendR()' function that serves as a drop-in
replacement for the upstream version but allows for additional
Imports: dplyr, forcats, gggibbous, ggimage, ggplot2, suncalc
Author: José Carlos Soage González [aut, cph],

Title: Create, Read, Write, and Work with 'iCalander' Files, Calendars
person("Robin", "Lovelace", email = "rob00x@gmail.com", role = c("aut", "cre"),
person("Layik", "Hama", email = "layik.hama@gmail.com", role = c("aut"),
Description: Provides function to create, read, write, and work with 'iCalander' files
(which typically have '.ics' or '.ical' extensions), and the scheduling data,
calendars and timelines of people, organisations and other entities that they represent.
exchanging calendar and scheduling information between users and computers,
Author: Robin Lovelace [aut, cre] (<https://orcid.org/0000-0001-5679-6536>),

Title: Statistical models and tools for assay calibration
Author: Perry Haaland <perry_haaland@bd.com>, Daniel Samarov <daniel_samarov@bd.com>,
Depends: R (>= 2.7.0), stats, nlme, methods
Description: Provides basic S4 data structures and routines for
calibration of bioassays. A variety of linear and nonlinear
calibration curves can be fit to data. Most models have a
Power of the Mean option for nonconstant variances. Unknown
concentrations can be predicted from the calibration curve.