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38 changes: 11 additions & 27 deletions DESCRIPTION
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Package: muHVT
Package: HVT
Type: Package
Date: 2023-07-07
Date: 2023-10-15
Title: Constructing Hierarchical Voronoi Tessellations and Overlay
Heatmap for Data Analysis
Version: (v23.06.07)
Version: 3.0.1
Authors@R: c(
person("Zubin", "Dowlaty", email = "[email protected]", role = "aut"),
person("Shubhra", "Prakash", email = "[email protected]", role = "ctb"),
person("Sangeet Moy", "Das", email = "[email protected]", role = "ctb"),
person("Sunuganty Achyut", "Raj", email = "[email protected]", role = "ctb"),
person("Shantanu", "Vaidya", email = "[email protected]", role = "ctb"),
person("Somya", "Shambhawi", email = "[email protected]", role = "ctb")
person("Praditi", "Shah", email = "[email protected]", role = "ctb"),
person("Avinash", "Joshi", email = "[email protected]", role = "ctb"),
person("Meet", "Dave", email = "[email protected]", role = "ctb"),
person("Mu Sigma, Inc.", email = "[email protected]", role = "cre"))
Description: The muHVT package is a collection of R functions to facilitate building topology preserving maps for rich multivariate data.See <https://en.wikipedia.org/wiki/Voronoi_diagram> for more information. Credits to Mu Sigma for their continuous support throughout the development of the package.
person("Zubin", "Dowlaty", email = "[email protected]", role = "aut"),
person("Mu Sigma, Inc.", email = "[email protected]", role = "cre"))
Description: The HVT package is a collection of R functions to facilitate building topology preserving maps for rich multivariate data.See <https://en.wikipedia.org/wiki/Voronoi_diagram> for more information. Credits to Mu Sigma for their continuous support throughout the development of the package.
License: Apache License 2.0
Encoding: UTF-8
Imports: MASS, deldir, grDevices, splancs, sp, conf.design, Hmisc,
stats, dplyr, purrr, magrittr, polyclip,
ggplot2, tidyr, scales, cluster, reshape2, plyr
stats, dplyr, purrr, magrittr, polyclip, ggplot2, tidyr,
scales, cluster, reshape2, plyr
Depends: R (>= 3.6.0)
BugReports: https://github.com/Mu-Sigma/muHVT/issues
URL: https://github.com/Mu-Sigma/muHVT
Expand All @@ -29,17 +21,9 @@ Suggests: knitr, rmarkdown, testthat, geozoo, kableExtra, plotly,
data.table
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2023-07-07 17:45:51 UTC; somya
Packaged: 2023-10-15 12:35:23 UTC; ponanureka
Author: Zubin Dowlaty [aut],
Shubhra Prakash [ctb],
Sangeet Moy Das [ctb],
Sunuganty Achyut Raj [ctb],
Shantanu Vaidya [ctb],
Praditi Shah [ctb],
Avinash Joshi [ctb],
Somya Shambhawi [ctb]
Meet Dave [ctb],
Mu Sigma, Inc. [cre]
Mu Sigma, Inc. [cre]
Maintainer: "Mu Sigma, Inc." <[email protected]>
Repository: CRAN
Date/Publication: 2023-07-07 20:10:03 UTC
Date/Publication: 2023-10-15 20:10:03 UTC
0 muHVT.Rproj → HVT.Rproj
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62 changes: 31 additions & 31 deletions README.md
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# muHVT: Collection of functions used to build hierarchical topology preserving maps
# HVT: Collection of functions used to build hierarchical topology preserving maps

#### Zubin Dowlaty, Shubhra Prakash, Sangeet Moy Das, Shantanu Vaidya, Praditi Shah, Srinivasan Sudarsanam, Somya Shambhawi
#### Zubin Dowlaty

#### 2023-06-07
#### 2023-10-15

<div id="TOC">

* [<span class="toc-section-number">1</span> Abstract](#abstract)
* [<span class="toc-section-number">2</span> Version History](#version-history)
* [<span class="toc-section-number">2.1</span> muHVT (v23.06.07) | What’s New?](#muhvt-(v23.06.07)-whats-new)
* [<span class="toc-section-number">2.2</span> muHVT (v22.12.06)](#muhvt-(v22.12.06))
* [<span class="toc-section-number">3</span> Installation of muHVT (v23.06.07)](#installation-of-muhvt-(v23.06.07))
* [<span class="toc-section-number">2.1</span> HVT (v23.10.15) | What’s New?](#hvt-(v23.10.15)-whats-new)
* [<span class="toc-section-number">2.2</span> HVT (v22.12.06)](#hvt-(v22.12.06))
* [<span class="toc-section-number">3</span> Installation of HVT (v23.10.15)](#installation-of-hvt-(v23.10.15))
* [<span class="toc-section-number">4</span> Vignettes](#vignettes)
* [<span class="toc-section-number">4.1</span> muHVT Vignette](#muhvt-vignette)
* [<span class="toc-section-number">4.2</span> muHVT Model Diagnostics Vignette](#muhvt-model-diagnostics-vignette)
* [<span class="toc-section-number">4.3</span> muHVT : Predicting Cells with Layers using predictLayerHVT ](#muhvt---predicting-cells-with-layers-using-predictLayerHVT)
* [<span class="toc-section-number">4.1</span> HVT Vignette](#hvt-vignette)
* [<span class="toc-section-number">4.2</span> HVT Model Diagnostics Vignette](#hvt-model-diagnostics-vignette)
* [<span class="toc-section-number">4.3</span> HVT : Predicting Cells with Layers using predictLayerHVT ](#hvt---predicting-cells-with-layers-using-predictLayerHVT)

</div>

<div id="abstract" class="section level1" number="1">

# <span class="header-section-number">1</span> Abstract

The muHVT package is a collection of R functions to facilitate building [topology preserving maps](https://users.ics.aalto.fi/jhollmen/dippa/node9.html#:~:text=The%20property%20of%20topology%20preserving,tool%20of%20high%2Ddimensional%20data) for rich multivariate data analysis, see `Figure 1` as an example of a 2D torus map generated from the package. Tending towards a big data preponderance, a large number of rows. A collection of R functions for this typical workflow is organized below:
The HVT package is a collection of R functions to facilitate building [topology preserving maps](https://users.ics.aalto.fi/jhollmen/dippa/node9.html#:~:text=The%20property%20of%20topology%20preserving,tool%20of%20high%2Ddimensional%20data) for rich multivariate data analysis, see `Figure 1` as an example of a 2D torus map generated from the package. Tending towards a big data preponderance, a large number of rows. A collection of R functions for this typical workflow is organized below:

1. **Data Compression**: Vector quantization (VQ), HVQ (hierarchical vector quantization) using means or medians. This step compresses the rows (long data frame) using a compression objective.

Expand All @@ -33,9 +33,9 @@ The muHVT package is a collection of R functions to facilitate building [topolog
4. **Prediction**: Scoring new data sets and recording their assignment using the map objects from the above steps, in a sequence of maps if required.


The muHVT package allows creation of visually stunning tessellations, showcasing the power of topology preserving maps. Below is an image depicting a captivating tessellation of a torus, see [vignette](https://htmlpreview.github.io/?https://github.com/Somya545/muHVT/blob/master/vignettes/muHVT_vignette.html) for more details.
The HVT package allows creation of visually stunning tessellations, showcasing the power of topology preserving maps. Below is an image depicting a captivating tessellation of a torus, see [vignette](https://htmlpreview.github.io/?https://github.com/Mu-Sigma/muHVT/blob/master/vignettes/HVT.html) for more details.

<img src="https://github.com/Somya545/muHVT/blob/master/vignettes/torus.png" width="642px" height="440px" />
<img src="https://github.com/Mu-Sigma/muHVT/blob/master/vignettes/torus.png" width="642px" height="440px" />
<p class="caption">
Figure 1: The Voronoi tessellation for layer 1 and number of cells 900 with the heat map overlaid for variable z.
</p>
Expand All @@ -47,20 +47,20 @@ Figure 1: The Voronoi tessellation for layer 1 and number of cells 900 with the

# <span class="header-section-number">2</span> Version History

<div id="muhvt-(v23.06.07)-whats-new" class="section level2" number="2.1">
<div id="hvt-(v23.10.15)-whats-new" class="section level2" number="2.1">

## <span class="header-section-number">2.1</span> muHVT (v23.06.07) | What’s New?
## <span class="header-section-number">2.1</span> HVT (v23.10.15) | What’s New?

07th June, 2023
15th October, 2023

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

This package provides functionality to predict cells with layers based on a sequence of maps using `predictLayerHVT`.
</div>

<div id="muhvt-(v22.12.06)" class="section level2" number="2.2">
<div id="hvt-(v22.12.06)" class="section level2" number="2.2">

## <span class="header-section-number">2.2</span> muHVT (v22.12.06)
## <span class="header-section-number">2.2</span> HVT (v22.12.06)

06th December, 2022

Expand All @@ -75,16 +75,16 @@ The creation of a predictive set of maps involves three steps -

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

<img src="https://github.com/Somya545/muHVT/blob/master/vignettes/predictLayerHVT_function.png" width="672px" height="480px" />
<img src="https://github.com/Mu-Sigma/muHVT/blob/master/vignettes/predictLayerHVT_function.png" width="672px" height="480px" />
<p class="caption">
Figure 2: Flow diagram for predicting based on a sequence of maps using predictLayerHVT()
</p>



<div id="installation-of-muhvt-(v23.06.07)" class="section level2" number="3">
<div id="installation-of-hvt-(v23.10.15)" class="section level2" number="3">

# <span class="header-section-number">3</span> Installation of muHVT (v23.06.07)
# <span class="header-section-number">3</span> Installation of HVT (v23.10.15)

<div class="sourceCode" id="cb1">

Expand All @@ -102,26 +102,26 @@ Figure 2: Flow diagram for predicting based on a sequence of maps using predictL

# <span class="header-section-number">4</span> Vignettes

Following are the links to the vignettes for the muHVT package:
Following are the links to the vignettes for the HVT package:

<div id="muhvt-vignette" class="section level2" number="4.1">
<div id="hvt-vignette" class="section level2" number="4.1">

## <span class="header-section-number">4.1</span> muHVT Vignette
## <span class="header-section-number">4.1</span> HVT Vignette

[**muHVT Vignette:**](https://htmlpreview.github.io/?https://github.com/Somya545/muHVT/blob/master/vignettes/muHVT_vignette.html) Contains descriptions of the functions used for vector quantization and construction of hierarchical voronoi tessellations for data analysis.
[**HVT Vignette:**](https://htmlpreview.github.io/?https://github.com/Mu-Sigma/muHVT/blob/master/vignettes/HVT.html) Contains descriptions of the functions used for vector quantization and construction of hierarchical voronoi tessellations for data analysis.

</div>

<div id="muhvt-model-diagnostics-vignette" class="section level2" number="4.2">
<div id="hvt-model-diagnostics-vignette" class="section level2" number="4.2">

## <span class="header-section-number">4.2</span> muHVT Model Diagnostics Vignette
## <span class="header-section-number">4.2</span> HVT Model Diagnostics Vignette

[**muHVT Model Diagnostics Vignette:**](https://htmlpreview.github.io/?https://github.com/Somya545/muHVT/blob/master/vignettes/muHVT_model_diagnostics_vignette.html) Contains descriptions of functions used to perform model diagnostics and validation for muHVT model.
[**HVT Model Diagnostics Vignette:**](https://htmlpreview.github.io/?https://github.com/Mu-Sigma/muHVT/blob/master/vignettes/HVT_model_diagnostics_vignette.html) Contains descriptions of functions used to perform model diagnostics and validation for HVT model.

</div>

<div id="muhvt---predicting-cells-with-layers-using-predictLayerHVT" class="section level2" number="4.3">
<div id="hvt---predicting-cells-with-layers-using-predictLayerHVT" class="section level2" number="4.3">

## <span class="header-section-number">4.3</span> muHVT - Predicting Cells with Layers using predictLayerHVT
## <span class="header-section-number">4.3</span> HVT - Predicting Cells with Layers using predictLayerHVT

[**muHVT : Predicting Cells with Layers using predictLayerHVT :**](https://htmlpreview.github.io/?https://github.com/Somya545/muHVT/blob/master/vignettes/Predicting_Cells_with_Layers_using_predictLayerHVT.html) Contains descriptions of the functions used for predicting cells with layers based on a sequence of maps using predictLayerHVT.
[**HVT : Predicting Cells with Layers using predictLayerHVT :**](https://htmlpreview.github.io/?https://github.com/Mu-Sigma/muHVT/blob/master/vignettes/Predicting_Cells_with_Layers_using_predictLayerHVT.html) Contains descriptions of the functions used for predicting cells with layers based on a sequence of maps using predictLayerHVT.
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4 changes: 2 additions & 2 deletions tests/testthat.R
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library(testthat)
library(muHVT)
library(HVT)

test_check("muHVT")
test_check("HVT")

8 changes: 4 additions & 4 deletions tests/testthat/testhvt.R
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Expand Up @@ -11,7 +11,7 @@ test_that("getCentroids give correct results for L1_Norm and mean",{
skip_on_cran()

set.seed(420)
hvt.results <- muHVT::HVT(USArrests,n_cells = 3,depth = 1,quant.err = 0.2,distance_metric = "L1_Norm",error_metric = "mean", quant_method = "kmeans")
hvt.results <- HVT::HVT(USArrests,n_cells = 3,depth = 1,quant.err = 0.2,distance_metric = "L1_Norm",error_metric = "mean", quant_method = "kmeans")

expect_equal(length(hvt.results),6)

Expand All @@ -26,7 +26,7 @@ test_that("getCentroids give correct results for L2_Norm and mean",{

set.seed(420)

hvt.results <- muHVT::HVT(USArrests,n_cells = 3,depth = 1,quant.err = 0.2,distance_metric = "L2_Norm",error_metric = "mean", quant_method = "kmeans")
hvt.results <- HVT::HVT(USArrests,n_cells = 3,depth = 1,quant.err = 0.2,distance_metric = "L2_Norm",error_metric = "mean", quant_method = "kmeans")

expect_equal(length(hvt.results),6)

Expand All @@ -40,7 +40,7 @@ test_that("getCentroids give correct results for L1_Norm and max",{

set.seed(420)

hvt.results <- muHVT::HVT(USArrests,n_cells = 3,depth = 1,quant.err = 0.2,distance_metric = "L1_Norm",error_metric = "max", quant_method = "kmeans")
hvt.results <- HVT::HVT(USArrests,n_cells = 3,depth = 1,quant.err = 0.2,distance_metric = "L1_Norm",error_metric = "max", quant_method = "kmeans")

expect_equal(length(hvt.results),6)

Expand All @@ -53,7 +53,7 @@ test_that("getCentroids give correct results for L2_Norm and max",{
skip_on_cran()

set.seed(420)
hvt.results <- muHVT::HVT(USArrests,n_cells = 3,depth = 1,quant.err = 0.2,distance_metric = "L2_Norm",error_metric = "max", quant_method = "kmeans")
hvt.results <- HVT::HVT(USArrests,n_cells = 3,depth = 1,quant.err = 0.2,distance_metric = "L2_Norm",error_metric = "max", quant_method = "kmeans")

expect_equal(length(hvt.results),6)

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