3.7 HVT (v25.2.6) - What’s New

14th October, 2025

In this version of the HVT package, the following new feature and vignette have been introduced:

Feature

1. Experimentation of hyperparameters in msm: This update introduces a new function called HVTMSMoptimization that runs grid search experiments across different hyperparameters (number of cells, clusters(k), nearest neighbors(nn)) by training and scoring HVT models, running MSM simulations for each combination. Returns the tabulated results and plotly object visualizations that highlight the champion model (i.e., the combination with lowest MAE).

Vignette

1. Hyperparameter Experimentation for Champion Model Selection in MSM Dynamic Forecasting: This vignette provides a comprehensive demonstration of using HVTMSMoptimization, covering the complete workflow from initial dataset handling, selection for train & test, executing hyperparameter tuning and identifying the champion model, implementing the champion model, and comparing MAE results.

The issue with time-series animation plots from previous release has now been resolved with the latest gganimate update.

3.6 HVT (v25.2.5)

04th July, 2025

Dropping the time-series animation plots from the package since the latest version of gganimate doesn’t support them — a patched release will follow once the issue is resolved.

3.5 HVT (v25.2.4)

04th June, 2025

In this version of the HVT package, the following new features and vignette have been introduced:

Features

1. Dynamic Forecasting of a Time Series Dataset: This update introduces a new function called msm Monte Carlo Simulations of Markov Chain for dynamic forecasting of states in time series dataset. It supports both ex-post and ex-ante forecasting, offering valuable insights into future trends while resolving state transition challenges through clustering and nearest-neighbor methods to enhance simulation accuracy.

2. Z score Plots: This update introduces a new function called plotZscore that generates Z-score plots corresponding to the HVT cells for the given data, offering a visual representation of data distribution and highlighting potential outliers.

Vignette

1. Dynamic Forecasting of Macroeconomic Time Series Dataset using HVT: This vignette illustrates the practical use of the new msm function on a macroeconomic dataset with 10 variables. It covers all steps, including data preparation, model training, scoring, and forecasting, while addressing challenges related to state transitions and evaluating performance using Mean Absolute Error (MAE).

3.4 HVT (v24.9.1)

4th September, 2024

In this version of the HVT package, the following new features and vignettes have been introduced:

Features

1. Implementation of t-SNE and UMAP in trainHVT: This update incorporates dimensionality reduction methods like t-SNE and UMAP in the trainHVT function, complementing the existing Sammon’s projection. It also enables the visualization of these techniques across all hierarchical levels within the HVT framework.

2. Implementation of dimensionality reduction evaluation metrics: This update introduces highly effective dimensionality reduction evaluation metrics as part of the output list of the trainHVT function. These metrics are organized into two levels: Level 1 (L1) and Level 2 (L2). The L1 metrics address key areas of dimensionality reduction which are mentioned below, by ensuring comprehensive evaluation and performance.

• Structure Preservation Metrics
• Distance Preservation Metrics
• Human Centered Metrics
• Interpretive Quality Metrics
• Computational Efficiency Metrics

3. Introduction of clustHVT function: In this update, we introduced a new function called clustHVT specifically designed for Hierarchical clustering analysis. The function performs clustering of cells exclusively when the hierarchy level is set to 1, determining the optimal number of clusters by evaluating various indices. Based on user input, it conducts hierarchical clustering using AGNES with the default ward.D2 method. The output includes a dendrogram and an interactive 2D clustered HVT map that reveals cell context upon hovering. This function is not applicable when the hierarchy level is greater than 1.

Vignettes

1. Implementation of t-SNE and UMAP in trainHVT function: This vignette showcases the integration of t-SNE and UMAP in the trainHVT function, offering a comprehensive guide on how to apply and visualize these dimensionality reduction techniques. It also covers the dimensionality reduction evaluation metrics and provides insights into their interpretation.

2. Visualizing LLM Embeddings using HVT (Hierarchical Voronoi Tessellation): This vignette will outline the process of analyzing OpenAI-generated token embeddings using the HVT package, covering data compression, visualization, and hierarchical clustering, as well as comparing domain name assignments for clusters. It examines HVT’s effectiveness in preserving contextual relationships between embeddings. Additionally, it provides a brief overview of the newly added clustHVT function and its parameters.

3.3 HVT (v24.5.2)

2nd May, 2024

In this version of HVT package, the following new features have been introduced:

1. Updated Nomenclature: To make the function names more consistent and understandable/intuitive, we have renamed the functions throughout the package. Given below are the few instances.

• HVT to trainHVT
• predictHVT to scoreHVT
• predictLayerHVT to scoreLayeredHVT

2. Restructured Functions: The functions have been rearranged and grouped into new sections which are highlighted on the index page of package’s PDF documentation. Given below are the few instances.

• trainHVT function now resides within the Training_or_Compression section.
• plotHVT function now resides within the Tessellation_and_Heatmap section.
• scoreHVT function now resides within the Scoring section.

3. Enhancements: The pre-existed functions, hvtHmap and exploded_hmap, have been combined and incorporated into the plotHVT function. Additionally, plotHVT now includes the ability to perform 1D plotting.

4. Temporal Analysis

• The new update focuses on the integration of time series capabilities into the HVT package by extending its foundational operations to time series data which is emphasized in this vignette.
• The new functionalities are introduced to analyze underlying patterns and trends within the data, providing insights into its evolution over time and also offering the capability to analyze the movement of the data by calculating its transitioning probability and creates elegant plots and GIFs.

Below are the new functions and its brief descriptions:

• plotStateTransition: Provides the time series flowmap plot.
• getTransitionProbability: Provides a list of transition probabilities.
• reconcileTransitionProbability: Provides plots and tables for comparing transition probabilities calculated manually and from markovchain function.
• plotAnimatedFlowmap: Creates flowmaps and animations for both self state and without self state scenarios.

3.2 HVT (v23.11.02)

17th November, 2023

This version of HVT package offers functionality to score cells with layers based on a sequence of maps created using scoreLayeredHVT. Given below are the steps to created the successive set of maps.

1. Map A - The output of trainHVT function which is trained on parent data.

2. Map B - The output of trainHVT function which is trained on the ‘data with novelty’ created from removeNovelty function.

3. Map C - The output of trainHVT function which is trained on the ‘data without novelty’ created from removeNovelty function.

The scoreLayeredHVT function uses these three maps to score the test datapoints.

Let us try to understand the steps with the help of the diagram below

Figure 2: Data Segregation for scoring based on a sequence of maps using scoreLayeredHVT()

3.1 HVT (v22.12.06)

06th December, 2022

This version of HVT package offers features for both training an HVT model and eliminating outlier cells from the trained model.

1. Training or Compression: The initial step entails training the parent data using the trainHVT function, specifying the desired compression percentage and quantization error.

2. Remove novelty cells: Following the training process, outlier cells can be identified manually from the 2D hvt plot. These outlier cells can then be inputted into the removeNovelty function, which subsequently produces two datasets in its output: one containing ‘data with novelty’ and the other containing ‘data without novelty’.

4. Installation of HVT (v25.2.6)