Package: SVMMaj 0.2.9.4

SVMMaj: Implementation of the SVM-Maj Algorithm

Implements the SVM-Maj algorithm to train data with support vector machine <doi:10.1007/s11634-008-0020-9>. This algorithm uses two efficient updates, one for linear kernel and one for the nonlinear kernel.

Authors:Hoksan Yip [aut, cre], Patrick J.F. Groenen [aut], Georgi Nalbantov [aut]

SVMMaj_0.2.9.4.tar.gz
SVMMaj_0.2.9.4.zip(r-4.7-any)SVMMaj_0.2.9.4.zip(r-4.6-any)SVMMaj_0.2.9.4.zip(r-4.5-any)
SVMMaj_0.2.9.4.tgz(r-4.6-any)SVMMaj_0.2.9.4.tgz(r-4.5-any)
SVMMaj_0.2.9.4.tar.gz(r-4.7-any)SVMMaj_0.2.9.4.tar.gz(r-4.6-any)
SVMMaj_0.2.9.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
SVMMaj/json (API)

# Install 'SVMMaj' in R:

install.packages('SVMMaj', repos = c('https://hoksanyip.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

AusCredit - Australian Credit Approval Dataset
AusCredit.te - Australian Credit Approval Dataset
AusCredit.tr - Australian Credit Approval Dataset
diabetes - Pima Indians Diabetes Data Set
diabetes.te - Pima Indians Diabetes Data Set
diabetes.tr - Pima Indians Diabetes Data Set
supermarket1996 - Supermarket data 1996
voting - Congressional Voting Records Data Set
voting.te - Congressional Voting Records Data Set
voting.tr - Congressional Voting Records Data Set

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.16 score 1 stars 29 scripts 307 downloads 9 exports 32 dependencies

Last updated from:4ec0c6b68b. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	170
source / vignettes	OK	255
linux-release-x86_64	OK	175
macos-release-arm64	OK	188
macos-oldrel-arm64	OK	217
windows-devel	OK	132
windows-release	OK	138
windows-oldrel	OK	118
wasm-release	OK	105

Exports:auc classification getHinge isb normalize plotWeights roccurve svmmaj svmmajcrossval

Dependencies:cli cpp11 dplyr farver generics ggplot2 glue gridExtra gtable isoband kernlab labeling lifecycle magrittr pillar pkgconfig plyr R6 RColorBrewer Rcpp reshape2 rlang S7 scales stringi stringr tibble tidyselect utf8 vctrs viridisLite withr

paper

Rendered frompaper.Rnwusingutils::Sweave

Last update: 2026-01-14
Started: 2018-02-25

Citation

Readme and manuals

Help Manual

Help page	Topics
Returns the area under the curve value	auc
Australian Credit Approval Dataset	AusCredit AusCredit.te AusCredit.tr
Show the classification performance	classification
Pima Indians Diabetes Data Set	diabetes diabetes.te diabetes.tr
Hinge error function of SVM-Maj	getHinge print.hinge
I-spline basis of each column of a given matrix	isb
Transform a given data into I-splines	isplinebasis
Normalize/standardize the columns of a matrix	normalize
Plot the hinge function	plot.hinge
Plot the cross validation output	plot.svmmajcrossval
Plot the weights of all attributes from the trained SVM model	plotWeights
Out-of-Sample Prediction from Unseen Data.	predict.svmmaj
Perform the transformation based on predefined settings	predict.transDat
SVM-Maj Algorithm	print.q.svmmaj svmmaj svmmaj.default
Print Svmmaj class	plot.svmmaj print.summary.svmmaj print.svmmaj summary.svmmaj
Print SVMMaj cross validation results	print.svmmajcrossval summary.svmmajcrossval
Plot the ROC curve of the predicted values	roccurve
Supermarket data 1996	supermarket1996
k-fold Cross-Validation of SVM-Maj	svmmajcrossval
Transform the data with normalization and/or spline basis	transformdata
Congressional Voting Records Data Set	voting voting.te voting.tr
Returns transformed attributes	X.svmmaj

Package: SVMMaj 0.2.9.4

SVMMaj: Implementation of the SVM-Maj Algorithm

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)