Force-Directed Euclidean Embedding of Dissimilarity Data
Perform Adaptive Monte Carlo Sampling
Add Noise and Bias to Matrix Data
Calculate Network Analysis Metrics
Calculate Annual Distance Metrics
Calculate Cumulative Distance Metrics
Calculate Adaptive Monte Carlo Sampling Diagnostics
Calculate prediction interval for distance estimates
Calculate Procrustes Difference Between Maps
Calculate Statistical Significance Between Maps Using Procrustes Analy...
Calculate Weighted Marginal Distributions
Check Multivariate Gaussian Convergence
Clean Data by Removing MAD-based Outliers
Color Palettes
Convert coordinates to distance matrix
Create and Optimize a RACMACS Map
Create Base Theme
Create Cross-validation Folds for Distance Matrix
Create Diagnostic Plots for Multiple Chains
Main TopoLow algorithm implementation
Detect Outliers Using Median Absolute Deviation
Convert Distance Matrix to Titer Panel Format
Calculate comprehensive error metrics between predicted and true dista...
Find Mode of Density Distribution
Generate Complex High-Dimensional Data for Testing
Generate New Parameter Samples Using KDE
Generate Synthetic Distance Matrices with Missing Data
Generate unique string identifiers with year suffix
Create Grid Around Maximum Likelihood Estimate
Save ggplot with white background
Sigmoid transform function for threshold handling
Increase Missing Values in a Matrix
Run Parameter Optimization Via Latin Hypercube Sampling
Evaluate Likelihood with Cross-Validation
Log Transform Parameter Samples
Convert Long Format Data to Distance Matrix
Create Interactive Plot
Plot Aesthetic Configuration Class
Plot Annotation Configuration Class
Dimension Reduction Configuration Class
Plot Layout Configuration Class
Filter matrix to only virus vs antiserum distances
Parameter Sensitivity Analysis
Create 3D Visualization
Create Clustered Mapping Plots
Plot Distance Matrix Heatmap
Plot Network Structure Analysis
Create Temporal Mapping Plot
Plot Method for Parameter Sensitivity Analysis
Plot Method for Profile Likelihood Objects
Plot Method for Adaptive Monte Carlo Sampling Diagnostics
Plot Method for Convergence Diagnostics
Generate Distance Matrix Heatmap Data
Print Method for Parameter Sensitivity Objects
Print Method for Profile Likelihood Objects
Print Method for Adaptive Monte Carlo Sampling Diagnostics
Print Method for Convergence Diagnostics
Print method for topolow objects
Process Raw Antigenic Assay Data
Process distance matrix for convergence error calculations
Profile Likelihood Analysis Results Class
Profile Likelihood Analysis
Prune Distance Data for Network Quality
Perform Dimension Reduction
Run Adaptive Monte Carlo Sampling
Sample from Weighted Distribution
Save Plot to File
Scale Reduced Dimensions to Match Original Distances
Plot Fitted vs True Distances
Summary method for topolow objects
Convert distance matrix to assay panel format
Latin Hypercube and Adaptive Monte Carlo Sampling Functions
Unweighted Kernel Density Estimation
Validate Antigenic Dataset
Validate Input Data Frame
Vectorized Processing of Distance Matrix for Convergence Error Calcula...
Weighted Kernel Density Estimation
Convert 2-digit to 4-digit year
A robust implementation of Topolow algorithm. It embeds objects into a low-dimensional Euclidean space from a matrix of pairwise dissimilarities, even when the data do not satisfy metric or Euclidean axioms. The package is particularly well-suited for sparse, incomplete, and censored (thresholded) datasets such as antigenic relationships. The core is a physics-inspired, gradient-free optimization framework that models objects as particles in a physical system, where observed dissimilarities define spring rest lengths and unobserved pairs exert repulsive forces. The package also provides functions specific to antigenic mapping to transform cross-reactivity and binding affinity measurements into accurate spatial representations in a phenotype space. Key features include: * Robust Embedding from Sparse Data: Effectively creates complete and consistent maps (in optimal dimensions) even with high proportions of missing data (e.g., >95%). * Physics-Inspired Optimization: Models objects (e.g., antigens, landmarks) as particles connected by springs (for measured dissimilarities) and subject to repulsive forces (for missing dissimilarities), and simulates the physical system using laws of mechanics, reducing the need for complex gradient computations. * Automatic Dimensionality Detection: Employs a likelihood-based approach to determine the optimal number of dimensions for the embedding/map, avoiding distortions common in methods with fixed low dimensions. * Noise and Bias Reduction: Naturally mitigates experimental noise and bias through its network-based, error-dampening mechanism. * Antigenic Velocity Calculation (for antigenic data): Introduces and quantifies "antigenic velocity," a vector that describes the rate and direction of antigenic drift for each pathogen isolate. This can help identify cluster transitions and potential lineage replacements. * Broad Applicability: Analyzes data from various objects that their dissimilarity may be of interest, ranging from complex biological measurements such as continuous and relational phenotypes, antibody-antigen interactions, and protein folding to abstract concepts, such as customer perception of different brands. Methods are described in the context of bioinformatics applications in Arhami and Rohani (2025a) <doi:10.1093/bioinformatics/btaf372>, and mathematical proofs and Euclidean embedding details are in Arhami and Rohani (2025b) <doi:10.48550/arXiv.2508.01733>.