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  "Title": "Fit Vector Fields and Potential Landscapes from Intensive\nLongitudinal Data",
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  "Authors@R": "person(\"Jingmeng\", \"Cui\", , \"jingmeng.cui@outlook.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-3421-8457\"))",
  "Description": "A toolbox for estimating vector fields from intensive\nlongitudinal data, and construct potential landscapes\nthereafter. The vector fields can be estimated with two\nnonparametric methods: the Multivariate Vector Field Kernel\nEstimator (MVKE) by Bandi & Moloche (2018)\n<doi:10.1017/S0266466617000305> and the Sparse Vector Field\nConsensus (SparseVFC) algorithm by Ma et al.  (2013)\n<doi:10.1016/j.patcog.2013.05.017>. The potential landscapes\ncan be constructed with a simulation-based approach with the\n'simlandr' package (Cui et al., 2021)\n<doi:10.31234/osf.io/pzva3>, or the Bhattacharya et al. (2011)\nmethod for path integration <doi:10.1186/1752-0509-5-85>.",
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