Package: MFSIS 0.2.1

Xuewei Cheng

MFSIS: Model-Free Sure Independent Screening Procedures

An implementation of popular screening methods that are commonly employed in ultra-high and high dimensional data. Through this publicly available package, we provide a unified framework to carry out model-free screening procedures including SIS (Fan and Lv (2008) <doi:10.1111/j.1467-9868.2008.00674.x>), SIRS(Zhu et al. (2011)<doi:10.1198/jasa.2011.tm10563>), DC-SIS (Li et al. (2012) <doi:10.1080/01621459.2012.695654>), MDC-SIS(Shao and Zhang (2014) <doi:10.1080/01621459.2014.887012>), Bcor-SIS (Pan et al. (2019) <doi:10.1080/01621459.2018.1462709>), PC-Screen (Liu et al. (2020) <doi:10.1080/01621459.2020.1783274>), WLS (Zhong et al.(2021) <doi:10.1080/01621459.2021.1918554>), Kfilter (Mai and Zou (2015) <doi:10.1214/14-AOS1303>), MVSIS (Cui et al. (2015) <doi:10.1080/01621459.2014.920256>), PSIS (Pan et al. (2016) <doi:10.1080/01621459.2014.998760>), CAS (Xie et al. (2020) <doi:10.1080/01621459.2019.1573734>), CI-SIS (Cheng and Wang. (2023) <doi:10.1016/j.cmpb.2022.107269>) and CSIS (Cheng et al. (2023) <doi:10.1007/s00180-023-01399-5>).

Authors:Xuewei Cheng [aut, cre], Hong Wang [aut], Liping Zhu [aut], Wei Zhong [aut], Hanpu Zhou [aut]

MFSIS_0.2.1.tar.gz
MFSIS_0.2.1.tar.gz(r-4.5-noble)MFSIS_0.2.1.tar.gz(r-4.4-noble)
MFSIS_0.2.1.tgz(r-4.4-emscripten)MFSIS_0.2.1.tgz(r-4.3-emscripten)
MFSIS.pdf |MFSIS.html✨
MFSIS/json (API)
NEWS

# Install 'MFSIS' in R:
install.packages('MFSIS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

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

2.00 score 2 stars 3 scripts 556 downloads 31 exports 24 dependencies

Last updated 6 months agofrom:75e310bb56. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 28 2024
R-4.5-linuxOKOct 28 2024

Exports:BcorSISCASCISISCorCSISDCSISGendataAFTGendataCoxGendataGPGendataIMGendataLDAGendataLGMGendataLMGendataMRMGendataPMGendataTMget_arccosKfilterKfilter_fusedKfilter_singleMDCSISMFSISMVSISPCSISprojection_corrPSISreq_pySimdataSIRSSISWLS

Dependencies:BallclicodetoolscrayondoParalleldrforeachgamhereiteratorsjsonlitelatticeMASSMatrixmvtnormpngrappdirsRcppRcppTOMLreticulaterlangrprojrootsurvivalwithr

Readme and manuals

Help Manual

Help pageTopics
A Generic Sure Independence Screening ProcedureBcorSIS
Category-Adaptive Variable Screening for Ultra-High Dimensional Heterogeneous Categorical DataCAS
Model-Free Feature screening Based on Concordance Index for Ultra-High Dimensional Categorical DataCISIS
Parallel function This is a parallel function about the projection correlation.Cor
Model-Free Feature screening Based on Concordance Index StatisticCSIS
Feature Screening via Distance Correlation LearningDCSIS
Generate simulation data (Survival data based on the accelerated failure time model)GendataAFT
Generate simulation data (Survival data based on the Cox model)GendataCox
Generate simulation data (Complete data with group predictors)GendataGP
Generate simulation data (Complete data for intersection variables)GendataIM
Generate simulation data (Categorial based on linear discriminant analysis model)GendataLDA
Generate simulation data (Binary category data based on logistic model)GendataLGM
Generate simulation data (Complete data based on linear models)GendataLM
Generate simulation data (Multivariate response models)GendataMRM
Generate simulation data (Discrete response data based on poisson model)GendataPM
Generate simulation data (Complete data based on transformation model)GendataTM
Arccos functionget_arccos
The Kolmogorov filter for variable screeningKfilter
The fused kolmogorov filter: a nonparametric model-free screening methodKfilter_fused
The Kolmogorov filter for variable screening in high-dimensional binary classificationKfilter_single
Martingale Difference Correlation and Its Use in High-Dimensional Variable ScreeningMDCSIS
Model-free feature screening proceduresMFSIS
Model-Free Feature Screening for Ultrahigh Dimensional Discriminant AnalysisMVSIS
Model-Free Feature Screening Based on the Projection CorrelationPCSIS
Projection correlation functionprojection_corr
Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence ScreeningPSIS
Detect Python Modulereq_py
Generate simulation data (The unified class framework to generate simulation data)Simdata
Model-Free Feature Screening for Ultrahigh Dimensional DataSIRS
Sure Independent ScreeningSIS
A Model-free Variable Screening Method Based on Leverage ScoreWLS