Package: quollr 0.2.0
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quollr: Visualising How Nonlinear Dimension Reduction Warps Your Data
To construct a model in 2D space from 2D embedding data and then lift it to the high-dimensional space. Additionally, it provides tools to visualize the model in 2D space and to overlay the fitted model on data using the tour technique. Furthermore, it facilitates the generation of summaries of high-dimensional distributions.
Authors:
quollr_0.2.0.tar.gz
quollr_0.2.0.zip(r-4.5)quollr_0.2.0.zip(r-4.4)quollr_0.2.0.zip(r-4.3)
quollr_0.2.0.tgz(r-4.5-any)quollr_0.2.0.tgz(r-4.4-any)quollr_0.2.0.tgz(r-4.3-any)
quollr_0.2.0.tar.gz(r-4.5-noble)quollr_0.2.0.tar.gz(r-4.4-noble)
quollr_0.2.0.tgz(r-4.4-emscripten)quollr_0.2.0.tgz(r-4.3-emscripten)
quollr.pdf |quollr.html✨
quollr/json (API)
NEWS
# Install 'quollr' in R: |
install.packages('quollr', repos = c('https://jayanilakshika.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jayanilakshika/quollr/issues
- s_curve_noise - S-curve dataset with noise dimensions
- s_curve_noise_test - S-curve dataset with noise dimensions for test
- s_curve_noise_training - S-curve dataset with noise dimensions for training
- s_curve_noise_umap - UMAP embedding for S-curve dataset which with noise dimensions
- s_curve_noise_umap_predict - Predicted UMAP embedding for S-curve dataset which with noise dimensions
- s_curve_noise_umap_scaled - Scaled UMAP embedding for S-curve dataset which with noise dimensions
Last updated 10 days agofrom:ee43798601. Checks:3 OK, 2 NOTE, 3 ERROR. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Feb 07 2025 |
R-4.5-win | ERROR | Feb 09 2025 |
R-4.5-mac | NOTE | Feb 07 2025 |
R-4.5-linux | NOTE | Feb 07 2025 |
R-4.4-win | ERROR | Feb 09 2025 |
R-4.4-mac | OK | Feb 07 2025 |
R-4.3-win | ERROR | Feb 09 2025 |
R-4.3-mac | OK | Feb 07 2025 |
Exports:assign_dataaugmentavg_highd_datacal_2d_distcalc_bins_ycompute_mean_density_hexcompute_std_countsextract_hexbin_centroidsextract_hexbin_meanfind_lg_benchmarkfind_low_dens_hexfind_non_empty_binsfind_ptsfit_highd_modelgen_centroidsgen_edgesgen_hex_coordgen_proj_langevitourgen_scaled_datageom_hexgridgeom_trimeshget_min_indicesglancehex_binningpredict_embshow_langevitourshow_link_plotsstat_hexgridstat_trimeshtri_bin_centroidsvis_lg_meshvis_rmlg_mesh
Dependencies:askpassassertthatbase64encbslibcachemclicodetoolscolorspacecpp11crosstalkcurldata.tabledeldirdigestdplyrevaluatefansifarverfastmapfontawesomefsfurrrfuturegenericsggplot2globalsgluegtablehighrhtmltoolshtmlwidgetshttrinterpisobandjquerylibjsonliteknitrlabelinglangevitourlaterlatticelazyevallifecyclelistenvmagrittrMASSMatrixmemoisemgcvmimemunsellnlmeopensslparallellypillarpkgconfigplotlypromisesproxypurrrR6RANNrappdirsRColorBrewerRcppRcppEigenrlangrmarkdownrsamplesassscalessliderstringistringrsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewarpwithrxfunyaml
Data preprocessing
Rendered fromquollr1dataprocessing.Rmd
usingknitr::rmarkdown
on Feb 07 2025.Last update: 2024-05-08
Started: 2024-02-29
Algorithm for visualising the model overlaid on high-dimensional data
Rendered fromquollr2algo.Rmd
usingknitr::rmarkdown
on Feb 07 2025.Last update: 2024-05-17
Started: 2024-02-29
Algorithm for binning data
Rendered fromquollr3hexbin.Rmd
usingknitr::rmarkdown
on Feb 07 2025.Last update: 2024-07-22
Started: 2024-02-29
Generating model summaries
Rendered fromquollr4summary.Rmd
usingknitr::rmarkdown
on Feb 07 2025.Last update: 2024-05-13
Started: 2024-02-29
Quick start
Rendered fromquollr5quickstart.Rmd
usingknitr::rmarkdown
on Feb 07 2025.Last update: 2024-05-16
Started: 2024-03-19