| Name : R-timedeppar
| |
| Version : 1.0.3
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.1.5
| Date : 2024-06-14 11:23:31
|
| Group : Development/Libraries/Other
| Source RPM : R-timedeppar-1.0.3-lp153.1.5.src.rpm
|
| Size : 0.18 MB
| |
| Packager : (none)
| |
| Summary : Infer Constant and Stochastic, Time-Dependent Model Parameters
|
Description :
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.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
