Pith. sign in

REVIEW 1 cited by

simode: R Package for statistical inference of ordinary differential equations using separable integral-matching

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1807.04202 v2 pith:ULRKFLSJ submitted 2018-07-11 stat.CO

classification stat.CO
keywords equationsdifferentialordinarypackageinferenceintegralintegral-matchingseparability
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

In this paper we describe simode: Separable Integral Matching for Ordinary Differential Equations. The statistical methodologies applied in the package focus on several minimization procedures of an integral-matching criterion function, taking advantage of the mathematical structure of the differential equations like separability of parameters from equations. Application of integral based methods to parameter estimation of ordinary differential equations was shown to yield more accurate and stable results comparing to derivative based ones. Linear features such as separability were shown to ease optimization and inference. We demonstrate the functionalities of the package using various systems of ordinary differential equations.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Separable nonlinear least-squares parameter estimation for complex dynamic systems

    stat.ME 2019-08 conditional novelty 4.0 of 10

    Separable nonlinear least squares, applied to integral-matching ODE estimation, matches or beats traditional least squares in most simulated scenarios and runs substantially faster.

Pith tools