Functions to fit nonparametric survival curves, plot them, and perform logrank or Wilcoxon type tests [see Fay and Shaw <doi:10.18637/jss.v036.i02>].

Perform fast and memory efficient time-weighted averaging of values 
    measured over intervals into new arbitrary intervals. 
	This package is useful in the context of data measured or represented
	as constant values over intervals on a one-dimensional discrete axis 
	(e.g. time-integrated averages of a curve over defined periods). 
	This package was written specifically to deal with air 
	pollution data recorded or predicted as averages over sampling periods. 
	Data in this format often needs to be shifted to non-aligned periods 
	or averaged up to periods of longer duration (e.g. averaging data measured over 
	sequential non-overlapping periods to calendar years).

Implements the Interval Consensus Model (ICM) for analyzing continuous bounded interval-valued responses in psychometrics using 'Stan' for 'Bayesian' estimation. Provides functions for transforming interval data to simplex representations, fitting item response theory (IRT) models with isometric log-ratio (ILR) and sum log-ratio (SLR) link functions, and visualizing results. The package enables aggregation and analysis of interval-valued response data commonly found in psychological measurement and related disciplines. Based on Kloft et al. (2024) <doi:10.31234/osf.io/dzvw2>.

A user-friendly toolbox for doing the statistical analysis of
  interval-valued responses in questionnaires measuring intrinsically
  imprecise human attributes or features (attitudes, perceptions, opinions,
  feelings, etc.). In particular, this package provides S4 classes, methods,
  and functions in order to compute basic arithmetic and statistical operations
  with interval-valued data; prepare customized plots; associate each
  interval-valued response to its equivalent Likert-type and visual analogue
  scales answers through the minimum theta-distance and the mid-point criteria;
  analyze the reliability of respondents' answers from the internal consistency
  point of view by means of Cronbach's alpha coefficient; and simulate
  interval-valued responses in this type of questionnaires. The package also
  incorporates some real-life data that can be used to illustrate its working
  with several non-trivial reproducible examples. The methodology used in this
  package is based in many theoretical and applied publications from
  SMIRE+CoDiRE (Statistical Methods with Imprecise Random Elements and
  Comparison of Distributions of Random Elements) Research Group
  (<https://bellman.ciencias.uniovi.es/smire+codire/>)
  from the University of Oviedo (Spain).