Package: HDNRA 2.1.0

Pengfei Wang

HDNRA: High-Dimensional Location Testing with Normal-Reference Approaches

Provides inverse-free high-dimensional location tests for two-sample and general linear hypothesis testing (GLHT) problems under equal or unequal covariance structures. The package implements classical normal-approximation procedures, scale-invariant procedures, normal-reference procedures based on covariance-matched Gaussian companions, and F-type normal-reference calibrations for heteroscedastic Behrens-Fisher and GLHT settings. Implemented two-sample normal-approximation and scale-invariant procedures include Bai and Saranadasa (1996) <https://www.jstor.org/stable/24306018>, Chen and Qin (2010) <doi:10.1214/09-aos716>, Srivastava and Du (2008) <doi:10.1016/j.jmva.2006.11.002>, and Srivastava et al. (2013) <doi:10.1016/j.jmva.2012.08.014>. Implemented two-sample normal-reference procedures include Zhang, Guo, Zhou and Cheng (2020) <doi:10.1080/01621459.2019.1604366>, Zhang, Zhou, Guo and Zhu (2021) <doi:10.1016/j.jspi.2020.11.008>, Zhang, Zhu and Zhang (2020) <doi:10.1016/j.ecosta.2019.12.002>, Zhang, Zhu and Zhang (2023) <doi:10.1080/02664763.2020.1834516>, Zhang and Zhu (2022) <doi:10.1080/10485252.2021.2015768>, Zhang and Zhu (2022) <doi:10.1007/s42519-021-00232-w>, and Zhu, Wang and Zhang (2023) <doi:10.1007/s00180-023-01433-6>. Implemented GLHT normal-approximation procedures include Fujikoshi et al. (2004) <doi:10.14490/jjss.34.19>, Srivastava and Fujikoshi (2006) <doi:10.1016/j.jmva.2005.08.010>, Yamada and Srivastava (2012) <doi:10.1080/03610926.2011.581786>, Schott (2007) <doi:10.1016/j.jmva.2006.11.007>, and Zhou, Guo and Zhang (2017) <doi:10.1016/j.jspi.2017.03.005>. Implemented GLHT normal-reference procedures include Zhang, Guo and Zhou (2017) <doi:10.1016/j.jmva.2017.01.002>, Zhang, Zhou and Guo (2022) <doi:10.1016/j.jmva.2021.104816>, Zhu, Zhang and Zhang (2022) <doi:10.5705/ss.202020.0362>, Zhu and Zhang (2022) <doi:10.1007/s00180-021-01110-6>, Zhang and Zhu (2022) <doi:10.1016/j.csda.2021.107385>, and Cao et al. (2024) <doi:10.1007/s00362-024-01530-8>. The package also includes the random-integration normal-approximation GLHT procedure of Li et al. (2025) <doi:10.1007/s00362-024-01624-3>. A package-level overview is given in Wang, Zhu and Zhang (2026) <doi:10.1016/j.csda.2025.108269>.

Authors:Pengfei Wang [aut, cre], Shuqi Luo [aut], Tianming Zhu [aut], Bu Zhou [aut]

HDNRA_2.1.0.tar.gz
HDNRA_2.1.0.tar.gz(r-4.7-arm64)HDNRA_2.1.0.tar.gz(r-4.7-x86_64)HDNRA_2.1.0.tar.gz(r-4.6-arm64)HDNRA_2.1.0.tar.gz(r-4.6-x86_64)
HDNRA_2.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
HDNRA/json (API)
NEWS

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

Bug tracker:https://github.com/nie23wp8738/hdnra/issues

Pkgdown/docs site:https://nie23wp8738.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

openblascppopenmp

1.85 score 14 scripts 479 downloads 25 exports 31 dependencies

Last updated from:9fbed3561e. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK186
linux-devel-x86_64OK180
source / vignettesOK252
linux-release-arm64OK188
linux-release-x86_64OK183
wasm-releaseOK151

Exports:BS1996.TS.NABTCCXH2024.GLHTBF.2cNRTCQ2010.TSBF.NABTFHW2004.GLHT.NABTLHNB2025.GLHTBF.NABTNRtest.objectS2007.ks.NABTSD2008.TS.NABTSF2006.GLHT.NABTSKK2013.TSBF.NABTWZ2026.GLHTBF.2cNRTYS2012.GLHT.NABTZGZ2017.GLHT.2cNRTZGZ2017.GLHTBF.NABTZGZC2020.TS.2cNRTZWZ2023.TSBF.2cNRTZZ2022.GLHT.3cNRTZZ2022.GLHTBF.3cNRTZZ2022.TS.3cNRTZZ2022.TSBF.3cNRTZZG2022.GLHTBF.2cNRTZZGZ2021.TSBF.2cNRTZZZ2020.TS.2cNRTZZZ2022.GLHT.2cNRTZZZ2023.TSBF.2cNRT

Dependencies:bitbit64clicliprcpp11crayonexpmgluehmslatticelifecyclemagrittrMatrixpillarpkgconfigprettyunitsprogressR6rbibutilsRcppRcppArmadilloRdpackreadrrlangtibbletidyselecttzdbutf8vctrsvroomwithr

Readme and manuals

Help Manual

Help pageTopics
Normal-approximation-based test for two-sample problem proposed by Bai and Saranadasa (1996)BS1996.TS.NABT
Normal-reference-test with two-cumulant (2-c) matched $\chi^2$-approximation for GLHT problem proposed by Cao et al. (2024)CCXH2024.GLHTBF.2cNRT
HDNRA_data cornealcorneal
HDNRA_data COVID19COVID19
Normal-approximation-based test for two-sample BF problem proposed by Chen and Qin (2010)CQ2010.TSBF.NABT
Normal-approximation-based test for GLHT problem proposed by Fujikoshi et al. (2004)FHW2004.GLHT.NABT
Normal-approximation-based test for k-sample linear hypothesis via random integration proposed by Li et al. (2025)LHNB2025.GLHTBF.NABT
S3 Class "NRtest"NRtest.object
Print Method for S3 Class "NRtest"print.NRtest
Normal-approximation-based test for one-way MANOVA problem proposed by Schott (2007)S2007.ks.NABT
Normal-approximation-based test for two-sample problem proposed by Srivastava and Du (2008)SD2008.TS.NABT
Normal-approximation-based test for GLHT problem proposed by Srivastava and Fujikoshi (2006)SF2006.GLHT.NABT
Normal-approximation-based test for two-sample BF problem proposed by Srivastava et al. (2013)SKK2013.TSBF.NABT
F-approximation-based F-type test for GLHT problem under heteroscedasticityWZ2026.GLHTBF.2cNRT
Normal-approximation-based test for GLHT problem proposed by Yamada and Srivastava (2012)YS2012.GLHT.NABT
Normal-reference-test with two-cumulant (2-c) matched $\chi^2$-approximation for GLHT problem proposed Zhang et al. (2017)ZGZ2017.GLHT.2cNRT
Normal-approximation-based test for GLHT problem under heteroscedasticity proposed by Zhou et al. (2017)ZGZ2017.GLHTBF.NABT
Normal-reference-test with two-cumulant (2-c) matched $\chi^2$-approximation for two-sample problem proposed by Zhang et al. (2020)ZGZC2020.TS.2cNRT
Normal-reference-test with two-cumulant (2-c) matched $\chi^2$-approximation for two-sample BF problem proposed by Zhu et al. (2023)ZWZ2023.TSBF.2cNRT
Normal-reference-test with three-cumulant (3-c) matched $\chi^2$-approximation for GLHT problem proposed by Zhu and Zhang (2022)ZZ2022.GLHT.3cNRT
Normal-reference-test with three-cumulant (3-c) matched $\chi^2$-approximation for GLHT problem under heteroscedasticity proposed by Zhang and Zhu (2022)ZZ2022.GLHTBF.3cNRT
Normal-reference-test with three-cumulant (3-c) matched $\chi^2$-approximation for two-sample problem proposed by Zhang and Zhu (2022)ZZ2022.TS.3cNRT
Normal-reference-test with three-cumulant (3-c) matched $\chi^2$-approximation for two-sample BF problem proposed by Zhang and Zhu (2022)ZZ2022.TSBF.3cNRT
Normal-reference-test with two-cumulant (2-c) matched $\chi^2$-approximation for GLHT problem under heteroscedasticity proposed by Zhang et al. (2022)ZZG2022.GLHTBF.2cNRT
Normal-reference-test with two-cumulant (2-c) matched $\chi^2$-approximation for two-sample BF problem proposed by Zhang et al. (2021)ZZGZ2021.TSBF.2cNRT
Normal-reference-test with two-cumulant (2-c) matched $\chi^2$-approximation for two-sample problem proposed by Zhang et al. (2020)ZZZ2020.TS.2cNRT
Normal-reference-test with two-cumulant (2-c) matched $\chi^2$-approximation for GLHT problem proposed by Zhu et al. (2022)ZZZ2022.GLHT.2cNRT
Normal-reference-test with two-cumulant (2-c) matched $\chi^2$-approximation for two-sample BF problem proposed by Zhang et al. (2023)ZZZ2023.TSBF.2cNRT