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resde: Estimation in Reducible Stochastic Differential Equations

Maximum likelihood estimation for univariate reducible stochastic differential equation models. Discrete, possibly noisy observations, not necessarily evenly spaced in time. Can fit multiple individuals/units with global and local parameters, by fixed-effects or mixed-effects methods. Ref.: Garcia, O. (2019) "Estimating reducible stochastic differential equations by conversion to a least-squares problem", Computational Statistics 34(1): 23-46, <doi:10.1007/s00180-018-0837-4>.

Version: 1.1
Imports: stats, Deriv, nlme, methods
Suggests: knitr
Published: 2023-05-19
Author: Oscar Garcia ORCID iD [aut, cre]
Maintainer: Oscar Garcia <garcia at dasometrics.net>
BugReports: https://github.com/ogarciav/resde/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/ogarciav/resde/
NeedsCompilation: no
Citation: resde citation info
Materials: NEWS
In views: DifferentialEquations, TimeSeries
CRAN checks: resde results

Documentation:

Reference manual: resde.pdf
Vignettes: Fitting Reducible SDE Models

Downloads:

Package source: resde_1.1.tar.gz
Windows binaries: r-devel: resde_1.1.zip, r-release: resde_1.1.zip, r-oldrel: resde_1.1.zip
macOS binaries: r-release (arm64): resde_1.1.tgz, r-oldrel (arm64): resde_1.1.tgz, r-release (x86_64): resde_1.1.tgz, r-oldrel (x86_64): resde_1.1.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=resde to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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