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 (1) panel cointegration rank tests, (2) estimators for panel
    vector autoregressive (VAR) models, and (3) identification methods for panel
    structural vector autoregressive (SVAR) models as described in the accompanying vignette.
    The implemented functions allow to account for cross-sectional dependence
    and for structural breaks in the deterministic terms of the VAR processes.
    Among the large set of functions, particularly noteworthy are those that implement
    (1) the correlation-augmented inverse normal test on the cointegration rank
    by Arsova and Oersal (2021, <doi:10.1016/j.ecosta.2020.05.002>),
    (2) the two-step estimator for pooled cointegrating vectors
    by Breitung (2005, <doi:10.1081/ETC-200067895>), and
    (3) the pooled identification based on independent component analysis
    by Herwartz and Wang (2024, <doi:10.1002/jae.3044>).