GeomTrimesh             GeomTrimesh: A Custom ggplot2 Geom for
                        Triangular Meshes
assign_data             Assign data to hexagons
avg_highd_data          Create a dataframe with averaged
                        high-dimensional data
cal_2d_dist             Calculate 2D Euclidean distances between
                        vertices
calc_bins               Calculate the effective number of bins along
                        x-axis and y-axis
calc_y_max              Compute maximum value of y for scaling
compute_aic             Compute the Akaike Information Criterion (AIC)
                        for a given model.
compute_mean_density_hex
                        Compute mean density of hexagonal bins
compute_std_counts      Compute standardize counts in hexagons
extract_hexbin_centroids
                        Extract hexagonal bin centroids coordinates and
                        the corresponding standardize counts.
find_lg_benchmark       Compute a benchmark value to remove long edges
find_low_dens_hex       Find low-density Hexagons
find_non_empty_bins     Find the number of bins required to achieve
                        required number of non-empty bins.
find_pts                Find points in hexagonal bins
fit_highd_model         Construct the 2D model and lift into high-D
gen_centroids           Generate centroid coordinate
gen_edges               Generate edge information
gen_hex_coord           Generate hexagonal polygon coordinates
gen_scaled_data         Scaling the data
gen_summary             Generate evaluation metrics
geom_trimesh            Create a trimesh plot
hex_binning             Hexagonal binning
predict_emb             Predict 2D embeddings
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
show_langevitour        Visualize the model overlaid on
                        high-dimensional data
stat_trimesh            stat_trimesh Custom Stat for trimesh plot
tri_bin_centroids       Triangulate bin centroids
vis_lg_mesh             Visualize triangular mesh with coloured long
                        edges
vis_rmlg_mesh           Visualize triangular mesh after removing the
                        long edges
