doi:10.1017/S0266466617000305> and the Sparse Vector Field Consensus (SparseVFC) algorithm by Ma et al. (2013) <doi:10.1016/j.patcog.2013.05.017>. The potential landscapes can be constructed with a simulation-based approach with the 'simlandr' package (Cui et al., 2021) <doi:10.31234/osf.io/pzva3>, or the Bhattacharya et al. (2011) method for path integration <doi:10.1186/1752-0509-5-85>.">

fitlandr: Fit Vector Fields and Potential Landscapes from Intensive Longitudinal Data (original) (raw)

A toolbox for estimating vector fields from intensive longitudinal data, and construct potential landscapes thereafter. The vector fields can be estimated with two nonparametric methods: the Multivariate Vector Field Kernel Estimator (MVKE) by Bandi & Moloche (2018) <doi:10.1017/S0266466617000305> and the Sparse Vector Field Consensus (SparseVFC) algorithm by Ma et al. (2013) <doi:10.1016/j.patcog.2013.05.017>. The potential landscapes can be constructed with a simulation-based approach with the 'simlandr' package (Cui et al., 2021) <doi:10.31234/osf.io/pzva3>, or the Bhattacharya et al. (2011) method for path integration <doi:10.1186/1752-0509-5-85>.

Version: 0.1.0
Imports: cli, dplyr, furrr, future.apply, ggplot2, glue, grDevices, grid, magrittr, MASS, numDeriv, plotly, R.utils, Rfast, rlang, rootSolve, simlandr (≥ 0.3.0), SparseVFC, tidyr
Suggests: akima, colorRamps, future
Published: 2023-02-10
DOI: 10.32614/CRAN.package.fitlandr
Author: Jingmeng Cui ORCID iD [aut, cre]
Maintainer: Jingmeng Cui <jingmeng.cui at outlook.com>
BugReports: https://github.com/Sciurus365/fitlandr/issues
License: GPL (≥ 3)
URL: https://sciurus365.github.io/fitlandr/,https://github.com/Sciurus365/fitlandr
NeedsCompilation: no
Materials: README NEWS
CRAN checks: fitlandr results

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