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001 | 872589 | ||
005 | 20210130004231.0 | ||
024 | 7 | _ | |a 10.21105/joss.01693 |2 doi |
024 | 7 | _ | |a 2128/24315 |2 Handle |
024 | 7 | _ | |a altmetric:73148822 |2 altmetric |
037 | _ | _ | |a FZJ-2020-00087 |
041 | _ | _ | |a English |
082 | _ | _ | |a 004 |
100 | 1 | _ | |a Gorjão, Leonardo |0 P:(DE-Juel1)173608 |b 0 |e Corresponding author |
245 | _ | _ | |a kramersmoyal: Kramers--Moyal coefficients for stochastic processes |
260 | _ | _ | |c 2019 |
336 | 7 | _ | |a article |2 DRIVER |
336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1581409326_29862 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a ARTICLE |2 BibTeX |
336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
520 | _ | _ | |a kramersmoyal is a python library to extract the Kramers--Moyal coefficients from timeseries of any dimension and to any desired order. This package employs a non-parametric Nadaraya--Watson estimator, i.e., kernel-density estimators, to retrieve the drift, diffusion, and higher-order moments of stochastic timeseries of any dimension. |
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588 | _ | _ | |a Dataset connected to CrossRef |
700 | 1 | _ | |a Meirinhos, Francisco |0 0000-0002-3864-7569 |b 1 |
773 | _ | _ | |a 10.21105/joss.01693 |g Vol. 4, no. 44, p. 1693 - |0 PERI:(DE-600)2891760-1 |n 44 |p 1693 - |t The journal of open source software |v 4 |y 2019 |x 2475-9066 |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/872589/files/Manuscript.pdf |y OpenAccess |
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