Provides function to create, read, write, and work with
    'iCalendar' files (which typically have '.ics' or '.ical' extensions),
    and the scheduling data, calendars and timelines of people,
    organisations and other entities that they represent.  'iCalendar' is
    an open standard for exchanging calendar and scheduling information
    between users and computers, described at <https://icalendar.org/>.

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.

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
  parameters unlocking extra functionality.

Contains greedy algorithms for coarse approximation linear functions.

Functions to impute using random forest under full conditional specifications (multivariate imputation by chained equations). The methods are described in Shah and others (2014) <doi:10.1093/aje/kwt312>.

Assess the calibration of an existing (i.e. previously developed) multistate
  model through calibration plots. 
  Calibration is assessed using one of three methods. 1) Calibration methods for 
  binary logistic regression models applied at a fixed time point in conjunction 
  with inverse probability of censoring weights. 2) Calibration methods for 
  multinomial logistic regression models applied at a fixed time point in conjunction 
  with inverse probability of censoring weights. 3) Pseudo-values estimated using 
  the Aalen-Johansen estimator of observed risk. All methods are applied in conjunction
  with landmarking when required. These calibration plots evaluate the calibration 
  (in a validation cohort of interest) of the transition probabilities estimated from an 
  existing multistate model. While package development has focused on multistate 
  models, calibration plots can be produced for any model which utilises information 
  post baseline to update predictions (e.g. dynamic models); competing risks models; 
  or standard single outcome survival models, where predictions can be made at 
  any landmark time. Please see Pate et al. (2024) <doi:10.1002/sim.10094>
  and Pate et al. (2024) <https://alexpate30.github.io/calibmsm/articles/Overview.html>.