Plot malaria parasite genetic data on two or more episodes. 
    Compute per-person posterior probabilities that each
    Plasmodium vivax (Pv) recurrence is a recrudescence, relapse,
    or reinfection (3Rs) using per-person P. vivax genetic data on two or
    more episodes and a statistical model described in 
    Taylor, Foo and White (2022) <doi:10.1101/2022.11.23.22282669>. 
    Plot per-recurrence posterior probabilities.

Likelihood based population viability analysis in the
  presence of observation error and missing data.
  The package can be used to fit, compare, predict,
  and forecast various growth model types using data cloning.

Contains functions to compute and plot confidence distributions, confidence densities, p-value functions and s-value (surprisal) functions for several commonly used estimates. Instead of just calculating one p-value and one confidence interval, p-value functions display p-values and confidence intervals for many levels thereby allowing to gauge the compatibility of several parameter values with the data. These methods are discussed by Infanger D, Schmidt-Trucksäss A. (2019) <doi:10.1002/sim.8293>; Poole C. (1987) <doi:10.2105/AJPH.77.2.195>; Schweder T, Hjort NL. (2002) <doi:10.1111/1467-9469.00285>; Bender R, Berg G, Zeeb H. (2005) <doi:10.1002/bimj.200410104> ; Singh K, Xie M, Strawderman WE. (2007) <doi:10.1214/074921707000000102>; Rothman KJ, Greenland S, Lash TL. (2008, ISBN:9781451190052); Amrhein V, Trafimow D, Greenland S. (2019) <doi:10.1080/00031305.2018.1543137>; Greenland S. (2019) <doi:10.1080/00031305.2018.1529625> and Rafi Z, Greenland S. (2020) <doi:10.1186/s12874-020-01105-9>.

The calculation of p-variation of the finite sample data. 
	This package is a realisation of the procedure described in Butkus, V. & Norvaisa, R. Lith Math J (2018). <doi: 10.1007/s10986-018-9414-3>
	The formal definitions and reference into literature are given in vignette.

Performs partial verification bias (PVB) correction for binary 
    diagnostic tests, where PVB arises from selective patient verification in 
    diagnostic accuracy studies. Supports correction of important accuracy
    measures -- sensitivity, specificity, positive predictive values and 
    negative predictive value -- under missing-at-random and 
    missing-not-at-random missing data mechanisms. Available methods and 
    references are "Begg and Greenes' methods" in Alonzo & Pepe (2005) 
    <doi:10.1111/j.1467-9876.2005.00477.x> and deGroot et al. (2011) 
    <doi:10.1016/j.annepidem.2010.10.004>; "Multiple imputation" in 
    Harel & Zhou (2006) <doi:10.1002/sim.2494>, "EM-based logistic regression" 
    in Kosinski & Barnhart (2003) <doi:10.1111/1541-0420.00019>; 
    "Inverse probability weighting" in Alonzo & Pepe (2005) 
    <doi:10.1111/j.1467-9876.2005.00477.x>; "Inverse probability bootstrap 
    sampling" in Nahorniak et al. (2015) <doi:10.1371/journal.pone.0131765> 
    and Arifin & Yusof (2022) <doi:10.3390/diagnostics12112839>; 
    "Scaled inverse probability resampling methods" in Arifin & Yusof (2025) 
    <doi:10.1371/journal.pone.0321440>.