Infer constant and stochastic, time-dependent parameters to consider intrinsic stochasticity of a dynamic model and/or to analyze model structure modifications that could reduce model deficits.
  The concept is based on inferring time-dependent parameters as stochastic processes in the form of Ornstein-Uhlenbeck processes jointly with inferring constant model parameters and parameters of the Ornstein-Uhlenbeck processes.
  The package also contains functions to sample from and calculate densities of Ornstein-Uhlenbeck processes.
  References:
  Tomassini, L., Reichert, P., Kuensch, H.-R. Buser, C., Knutti, R. and Borsuk, M.E. (2009), A smoothing algorithm for estimating stochastic, continuous-time model parameters and its application to a simple climate model, Journal of the Royal Statistical Society: Series C (Applied Statistics) 58, 679-704, <doi:10.1111/j.1467-9876.2009.00678.x>
  Reichert, P., and Mieleitner, J. (2009), Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters. Water Resources Research, 45, W10402, <doi:10.1029/2009WR007814>
  Reichert, P., Ammann, L. and Fenicia, F. (2021), Potential and challenges of investigating intrinsic uncertainty of hydrological models with time-dependent, stochastic parameters. Water Resources Research 57(8), e2020WR028311, <doi:10.1029/2020WR028311>
  Reichert, P. (2022), timedeppar: An R package for inferring stochastic, time-dependent model parameters, in preparation.

Data frames with time information are subset and flagged with period information. Data frames with times are dealt as timeDF objects and periods are represented as periodDF objects.

Computation of t-year survival probabilities and t-year
    risks with right censored survival data. The Kaplan-Meier estimator
    is used to provide estimates for data without competing risks and
    the Aalen-Johansen estimator is used when there are competing risks.
    Confidence intervals and p-values are obtained using either usual
    Wald-type inference or empirical likelihood inference, as described
    in Thomas and Grunkemeier (1975) <doi:10.1080/01621459.1975.10480315>
    and Blanche (2020) <doi:10.1007/s10985-018-09458-6>. Functions for
    both one-sample and two-sample inference are provided. Unlike Wald-type
    inference, empirical likelihood inference always leads to consistent
    conclusions, in terms of statistical significance, when comparing
    two risks (or survival probabilities) via either a ratio or a difference.