R Observatory: Metrics for ICompELM version 0.1.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

ICompELM

0.1.0

Independent Component Analysis Based Extreme Learning Machine

0packages depend

6Kdownloads / year

–test coverage

13/13checks pass

Overview

About

Maintained by Saikath DasFirst published 2024-06-101 releasesCRAN page ↗

Single Layer Feed-forward Neural networks (SLFNs) have many applications in various fields of statistical modelling, especially for time-series forecasting. However, there are some major disadvantages of training such networks via the widely accepted 'gradient-based backpropagation' algorithm, such as convergence to local minima, dependencies on learning rate and large training time. These concerns were addressed by Huang et al. (2006) doi:10.1016/j.neucom.2005.12.126, wherein they introduced the Extreme Learning Machine (ELM), an extremely fast learning algorithm for SLFNs which randomly chooses the weights connecting input and hidden nodes and analytically determines the output weights of SLFNs. It shows good generalized performance, but is still subject to a high degree of randomness. To mitigate this issue, this package uses a dimensionality reduction technique given in Hyvarinen (1999) doi:10.1109/72.761722, namely, the Independent Component Analysis (ICA) to determine the input-hidden connections and thus, remove any sort of randomness from the algorithm. This leads to a robust, fast and stable ELM model. Using functions within this package, the proposed model can also be compared with an existing alternative based on the Principal Component Analysis (PCA) algorithm given by Pearson (1901) doi:10.1080/14786440109462720, i.e., the PCA based ELM model given by Castano et al. (2013) doi:10.1007/s11063-012-9253-x, from which the implemented ICA based algorithm is greatly inspired.

Install

R (CRAN)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("ICompELM", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-icompelm

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-ICompELM

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-ICompELM

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed).

Health

CRAN checks

13OK

Slowest check: 55 s · r-oldrel-windows-x86_64

Code health

None

Tests · ratio 0.00

not tracked

Coverage

100%

Documentation · exports

3

Dependencies · direct

Check history

OK2026-08-05
13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
NOTE2026-08-01
12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
OK2026-06-09
13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
ERROR2026-06-08
12 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
OK2026-03-10
14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMENoVignettesNopkgdown siteNoNEWSNoCode of conductNoContributing guideNo

Examples that run: 100%
Documented parameters: 100%
Return-value docs: 100%
References docs: 40%

Downloads

6K

CRAN downloads in the past year

Rank #8,250 · ~16/day · ~499/mo

Releases R versions

Daily download trend is not available in this view yet.

16130 days

1.1K90 days

6K1 year

Compare downloads with other packages →

Also on96 r2u11 autocran

Dependencies

Declared dependencies

3 external dependencies (excludes base and recommended)

Depends (1)

R >= 3.5.0

Imports (3)

statstsutils ica

LinkingTo (0)

none

Suggests (1)

forecast

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

Code & Tests

Datasets

People & History

People (4)

Maintainer (1)

Saikath Das

Author, Maintainer

Authors (4)

Author, Maintainer

Author

Author

Author

Package Timeline

1 release. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
0.1.0Latest
2026-03-10 · current release
R
R 4.5.0 released · 2025-04-11

Package metadata

First published: 2024-06-10
Total releases: 1 / 2 yrs
License: GPL-3 OSI
Minimum R: ≥ 3.5.0
Bundled data: 1.3 KB / 1 file
Download size: 8.1 KB
Installed size: not tracked yet
With dependencies: not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("ICompELM")

Format

Das, S., Paul, A. K., Paul, R. K., & Yeasin, M. (2024). ICompELM: Independent Component Analysis Based Extreme Learning Machine (Version 0.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.ICompELM

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for ICompELM version 0.1.0 [Data set]. HJJB, LLC. Data release v2026-08-15. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-15, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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