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.

Implements the estimator of Tugan (2021) <doi:10.1093/ectj/utaa021>
    for panel vector autoregression (VAR) models with interactive fixed effects.
    Provides joint estimation of VAR coefficients, latent common factors, and
    factor loadings via an iterative algorithm that alternates between principal
    component estimation of the factors and least squares estimation of the VAR
    coefficients, following the approach of Bai (2009) <doi:10.3982/ECTA6135>.
    Supports
    impulse response functions under recursive (Cholesky) identification,
    parametric confidence bands from the joint asymptotic distribution of the
    estimator (Theorem 2.3), and a classical residual bootstrap for robustness
    checks.