Package: fitlandr 0.1.0.9000
fitlandr: Fit Vector Fields and Potential Landscapes from Intensive Longitudinal Data
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>.
Authors:
fitlandr_0.1.0.9000.tar.gz
fitlandr_0.1.0.9000.zip(r-4.5)fitlandr_0.1.0.9000.zip(r-4.4)fitlandr_0.1.0.9000.zip(r-4.3)
fitlandr_0.1.0.9000.tgz(r-4.5-any)fitlandr_0.1.0.9000.tgz(r-4.4-any)fitlandr_0.1.0.9000.tgz(r-4.3-any)
fitlandr_0.1.0.9000.tar.gz(r-4.5-noble)fitlandr_0.1.0.9000.tar.gz(r-4.4-noble)
fitlandr_0.1.0.9000.tgz(r-4.4-emscripten)fitlandr_0.1.0.9000.tgz(r-4.3-emscripten)
fitlandr.pdf |fitlandr.html✨
fitlandr/json (API)
NEWS
| # Install 'fitlandr' in R: |
| install.packages('fitlandr', repos = c('https://sciurus365.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/sciurus365/fitlandr/issues
Pkgdown site:https://sciurus365.github.io
Last updated 5 months agofrom:23ad8c8800. Checks:6 OK, 3 NOTE. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 22 2025 |
| R-4.5-win | OK | Mar 22 2025 |
| R-4.5-mac | OK | Mar 22 2025 |
| R-4.5-linux | OK | Mar 22 2025 |
| R-4.4-win | NOTE | Mar 22 2025 |
| R-4.4-mac | NOTE | Mar 22 2025 |
| R-4.4-linux | NOTE | Mar 22 2025 |
| R-4.3-win | OK | Mar 22 2025 |
| R-4.3-mac | OK | Mar 22 2025 |
Exports:%>%add_interp_gridalign_pot_Bfast_bilinearfind_eqsfit_2d_ldfit_2d_vffit_3d_vfldMVKEpath_integral_BpathB_optionsreorder_outputsim_vfsim_vf_optionssimlandr_options
Dependencies:askpassbase64encBHbigmemorybigmemory.sribslibcachemclassclassIntclicodacodetoolscolorspacecpp11crayoncrosstalkcurldata.tableDBIDerivdigestdplyre1071evaluatefansifarverfastmapFNNfontawesomeforcatsfsfurrrfuturefuture.applygenericsgganimateggplot2globalsgluegtablehighrhmshtmltoolshtmlwidgetshttrisobandjquerylibjsonlitekernlabKernSmoothknitrkslabelinglaterlatticelazyevallifecyclelistenvlpSolvemagrittrMASSMatrixmclustmemoisemgcvmimemulticoolmunsellmvtnormnlmenumDerivopensslparallellypdistpillarpkgconfigplotlypracmaprettyunitsprogresspromisesproxypurrrR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppRcppArmadilloRcppParallelRfastrlangrmarkdownrootSolves2sassscalessfSim.DiffProcsimlandrSparseVFCstringistringrsystibbletidyrtidyselecttinytextransformrtweenrunitsutf8uuidvctrsviridisLitewithrwkxfunyamlzigg
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Add a grid to a 'vectorfield' object to enable linear interpolation | add_interp_grid |
| Find equilibrium points for a vector field | find_eqs |
| Estimate a 2D potential landscape from data with the MVKE method | fit_2d_ld summary.2d_MVKE_landscape |
| Estimate a 2D vector field | fit_2d_vf |
| Estimate a 3D potential landscape from a vector field | fit_3d_vfld |
| Multivariate vector field kernel estimator | MVKE |
| Return a normalized prediction function | normalize_predict_f |
| Options controlling the path-integral algorithm | pathB_options |
| Plot a 2D vector field | plot.vectorfield |
| Calculate the vector value at a given position | predict.vectorfield |
| Reorder a simulation output in time order | reorder_output |
| Simulation from vector fields | sim_vf |
| Options controlling the vector field simulation | sim_vf_options |
| Options controlling the landscape construction | simlandr_options |
