Package: TULIP 1.0.2

TULIP: A Toolbox for Linear Discriminant Analysis with Penalties

Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <arxiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.

Authors:Yuqing Pan <[email protected]>, Qing Mai <[email protected]>, Xin Zhang <[email protected]>

TULIP_1.0.2.tar.gz
TULIP_1.0.2.zip(r-4.7-x86_64)TULIP_1.0.2.zip(r-4.7-arm64)TULIP_1.0.2.zip(r-4.6-x86_64)TULIP_1.0.2.zip(r-4.6-arm64)TULIP_1.0.2.zip(r-4.5-x86_64)
TULIP_1.0.2.tgz(r-4.6-x86_64)TULIP_1.0.2.tgz(r-4.6-arm64)TULIP_1.0.2.tgz(r-4.5-x86_64)TULIP_1.0.2.tgz(r-4.5-arm64)
TULIP_1.0.2.tar.gz(r-4.7-arm64)TULIP_1.0.2.tar.gz(r-4.7-x86_64)TULIP_1.0.2.tar.gz(r-4.6-arm64)TULIP_1.0.2.tar.gz(r-4.6-x86_64)
TULIP_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
TULIP/json (API)

# Install 'TULIP' in R:

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

Uses libs:

fortran– Runtime library for GNU Fortran applications

Datasets:

csa - Colorimetric sensor array (CSA) data
GDS1615 - GDS1615 data introduced in Burczynski et al. (2012).

On CRAN:

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

fortran

2.00 score 10 scripts 288 downloads 20 mentions 21 exports 13 dependencies

Last updated from:0b771e74a2. Checks:14 OK, 1 WARNING. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	107
linux-devel-x86_64	OK	117
source / vignettes	OK	184
linux-release-arm64	OK	102
linux-release-x86_64	OK	140
macos-release-arm64	OK	178
macos-release-x86_64	OK	373
macos-oldrel-arm64	OK	174
macos-oldrel-x86_64	OK	338
windows-devel-arm64	OK	352
windows-devel-x86_64	OK	135
windows-release-arm64	WARNING	315
windows-release-x86_64	OK	92
windows-oldrel-x86_64	OK	111
wasm-release	OK	78

Exports:adjten adjvec catch catch_matrix cv.catch cv.dsda cv.msda cv.SeSDA dsda dsda.all getnorm msda predict.catch predict.dsda predict.msda predict.SeSDA ROAD SeSDA sim.bi.vector sim.tensor.cov SOS

Dependencies:assertthat codetools foreach glmnet iterators lattice MASS Matrix Rcpp RcppEigen shape survival tensr

Help page	Topics
Adjust tensor for covariates.	adjten
Adjust vector for covariates.	adjvec
Fit a CATCH model and predict categorical response.	catch
Fit a CATCH model for matrix and predict categorical response.	catch_matrix
Colorimetric sensor array (CSA) data	csa
Cross-validation for CATCH	cv.catch
Cross validation for direct sparse discriminant analysis	cv.dsda
Cross-validation for DSDA/MSDA through function 'msda'	cv.msda
Cross validation for semiparametric sparse discriminant analysis	cv.SeSDA
Solution path for direct sparse discriminant analysis	dsda
Direct sparse discriminant analysis	dsda.all
GDS1615 data introduced in Burczynski et al. (2012).	GDS1615 x y
Direct sparse discriminant analysis	getnorm
Fits a regularization path of Sparse Discriminant Analysis and predicts	msda
Predict categorical responses for matrix/tensor data.	predict.catch
Prediction for direct sparse discriminant analysis	predict.dsda
Predict categorical responses for vector data.	predict.msda
Prediction for semiparametric sparse discriminant analysis	predict.SeSDA
Solution path for regularized optimal affine discriminant	ROAD
Solution path for semiparametric sparse discriminant analysis	SeSDA
Simulate data	sim.bi.vector
Simulate data	sim.tensor.cov
Solution path for sparse discriminant analysis	SOS

Package: TULIP 1.0.2

TULIP: A Toolbox for Linear Discriminant Analysis with Penalties

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)