Package: quollr 1.0.6

quollr: Visualising How Nonlinear Dimension Reduction Warps Your Data

To construct a model in 2-D space from 2-D nonlinear dimension reduction data and then lift it to the high-dimensional space. Additionally, provides tools to visualise the model overlay the data in 2-D and high-dimensional space. Furthermore, provides summaries and diagnostics to evaluate the nonlinear dimension reduction layout.

Authors:Jayani P. Gamage [aut, cre], Dianne Cook [aut], Paul Harrison [aut], Michael Lydeamore [aut], Thiyanga S. Talagala [aut]

quollr_1.0.6.tar.gz
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quollr_1.0.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
quollr/json (API)

# 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

Pkgdown/docs site:https://jayanilakshika.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

8.25 score 8 stars 1 packages 115 scripts 187 downloads 39 exports 82 dependencies

Last updated from:41fa2ad7df. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK347
linux-devel-x86_64OK205
source / vignettesOK275
linux-release-arm64OK192
linux-release-x86_64OK187
macos-release-arm64OK144
macos-release-x86_64OK261
macos-oldrel-arm64OK199
macos-oldrel-x86_64OK531
windows-develOK198
windows-releaseOK158
windows-oldrelOK184
wasm-releaseOK162

Exports:assign_dataaugmentavg_highd_datacalc_2d_distcalc_bins_ycomb_all_data_modelcomb_all_data_model_errorcomb_data_modelcompute_mean_density_hexcompute_std_countsfind_low_dens_hexfind_non_empty_binsfit_highd_modelgen_axesgen_centroidsgen_designgen_diffbin1_errorsgen_edgesgen_hex_coordgen_scaled_datageom_hexgridgeom_trimeshget_projectionglancegroup_hex_ptshex_binningmerge_hexbin_centroidsmerge_hexbin_meanplot_hbe_layoutsplot_projpredict_embquadshow_error_link_plotsshow_langevitourshow_link_plotsstat_hexgridstat_trimeshtri_bin_centroidsupdate_trimesh_index

Dependencies:askpassassertthatbase64encbslibcachemclicodetoolscpp11crosstalkcurldata.tabledeldirdigestdplyrevaluatefarverfastmapfontawesomefsfurrrfuturegenericsggplot2globalsgluegtablehighrhtmltoolshtmlwidgetshttrinterpisobandjquerylibjsonliteknitrlabelinglangevitourlaterlazyevallifecyclelistenvmagrittrmemoisemimeopensslotelparallellypatchworkpillarpkgconfigplotlypromisesproxypurrrR6RANNrappdirsRColorBrewerRcppRcppArmadilloRcppEigenrlangrmarkdownrsampleS7sassscalessliderstringistringrsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewarpwithrxfunyaml

Linked plots with detourr
Fitting the Model | Two-Panel Linked View: NLDR Layout and Tour | Three-Panel Linked View: Adding Model Error
Rendered fromquollr8linkeddetourr.Rmdusingknitr::rmarkdown

Last update: 2025-12-15
Started: 2025-12-14

Quick start
Rendered fromquollr1quickstart.Rmdusingknitr::rmarkdown

Last update: 2025-12-14
Started: 2025-07-22

Data preprocessing
Rendered fromquollr2dataprocessing.Rmdusingknitr::rmarkdown

Last update: 2025-12-14
Started: 2025-07-22

Algorithm for binning data
Rendered fromquollr3hexbin.Rmdusingknitr::rmarkdown

Last update: 2025-12-14
Started: 2024-02-29

Algorithm for visualising the model overlaid on high-dimensional data
Step 1: Construct the 2-D model | Hexagonal Binning | Extract bin centroids | Triangulate the bin centroids | Generate edges from triangulation | Visualise the triangular mesh | Step 2: Lift the model into high dimensions | Map bins to high-dimensional observations | Compute high-dimensional coordinates for bins | Step 3: Visualise the high-dimensional model | Prepare data for visualisation | Interactive tour of model overlay
Rendered fromquollr4algo.Rmdusingknitr::rmarkdown

Last update: 2025-12-14
Started: 2025-07-22

Generating model summaries
Step 1: Fitting the model | Step 2: Predicting 2-D embedding for data | Visualising predictions | Step 3: Computing model summaries | Step 4: Augmenting the dataset
Rendered fromquollr5summary.Rmdusingknitr::rmarkdown

Last update: 2025-12-14
Started: 2025-07-22

Selecting the optimal bin width
Rendered fromquollr6bestbinwidth.Rmdusingknitr::rmarkdown

Last update: 2025-12-14
Started: 2025-05-05

Selecting the best fit
Step 1: Generate design for 2-D NLDR layouts | Step 2: Visualising HBE across configurations
Rendered fromquollr7bestfit.Rmdusingknitr::rmarkdown

Last update: 2025-10-26
Started: 2025-05-05

Readme and manuals

Help Manual

Help pageTopics
Assign data to hexagonsassign_data
S3 generic for augmentaugment
Augment Data with Predictions and Error Metrics for NLDR Modelsaugment.highd_vis_model
Create a tibble with averaged high-dimensional dataavg_highd_data
Calculate 2-D Euclidean distances between verticescalc_2d_dist
Calculate the effective number of bins along x-axis and y-axiscalc_bins_y
Create a tibble with averaged high-dimensional data and high-dimensional data, non-linear dimension reduction datacomb_all_data_model
Create a tibble with averaged high-dimensional data and high-dimensional data, non-linear dimension reduction data, model error datacomb_all_data_model_error
Create a tibble with averaged high-dimensional data and high-dimensional datacomb_data_model
Compute mean density of hexagonal binscompute_mean_density_hex
Compute standardise counts in hexagonscompute_std_counts
Find low-density Hexagonsfind_low_dens_hex
Find the number of bins required to achieve required number of non-empty bins.find_non_empty_bins
Construct the 2-D model and lift into high-dimensionsfit_highd_model
Generate Axes for Projectiongen_axes
Generate centroid coordinategen_centroids
Generate a design to layout 2-D representationsgen_design
Generate erros and MSE for different bin widthsgen_diffbin1_errors
Generate edge informationgen_edges
Generate hexagonal polygon coordinatesgen_hex_coord
Scaling the NLDR datagen_scaled_data
Create a hexgrid plotgeom_hexgrid
Create a trimesh plotgeom_trimesh
GeomHexgrid: A Custom ggplot2 Geom for Hexagonal GridGeomHexgrid
GeomTrimesh: A Custom ggplot2 Geom for Triangular MeshesGeomTrimesh
Compute Projection for High-Dimensional Dataget_projection
S3 generic for glanceglance
Generate evaluation metrics for a hex_model objectglance.highd_vis_model
Grouped points in each hexagongroup_hex_pts
Hexagonal binninghex_binning
Extract hexagonal bin centroids coordinates and the corresponding standardise counts.merge_hexbin_centroids
Extract hexagonal bin mean coordinates and the corresponding standardize counts.merge_hexbin_mean
Arrange HBE plot and 2-D layoutsplot_hbe_layouts
Plot Projected Data with Axes and Circlesplot_proj
Predict 2-D embeddingspredict_emb
Solve Quadratic Equation for Positive Real Rootsquad
quollr: Visualising How Nonlinear Dimension Reduction Warps Your Dataquollr-package quollr
S-curve dataset with noise dimensionsscurve
Object for S-curve datasetscurve_model_obj
List of plotsscurve_plts
UMAP embedding for `scurve` with n_neighbors = 15 and min_dist = 0.1scurve_umap
Predicted UMAP embedding for `scurve` datascurve_umap_predict
Summary with different number of bins for `scurve_umap`scurve_umap_rmse
Summary with different number of bins for `scurve_umap2`scurve_umap_rmse2
Summary with different number of bins for `scurve_umap3`scurve_umap_rmse3
Summary with different number of bins for `scurve_umap4`scurve_umap_rmse4
UMAP embedding for `scurve` with n_neighbors = 10 and min_dist = 0.4scurve_umap2
UMAP embedding for `scurve` with n_neighbors = 62 and min_dist = 0.1scurve_umap3
UMAP embedding for `scurve` with n_neighbors = 30 and min_dist = 0.5scurve_umap4
Visualise the model overlaid on high-dimensional data along with 2-D wireframe model and error.show_error_link_plots
Visualise the model overlaid on high-dimensional datashow_langevitour
Visualise the model overlaid on high-dimensional data along with 2-D wireframe model.show_link_plots
stat_hexgrid Custom Stat for hexagonal grid plotstat_hexgrid
stat_trimesh Custom Stat for trimesh plotstat_trimesh
Triangulate bin centroidstri_bin_centroids
Update from and to values in trimesh dataupdate_trimesh_index