Package: KMT 1.0.0

Jiwoong Kim

KMT: Khmaladze Martingale Transformation Goodness-of-Fit Test

Consider a goodness-of-fit problem of testing whether a random sample comes from one sample location-scale model where location and scale parameters are unknown. It is well known that Khmaladze-martingale-transformation method proposed by Khmaladze (1981) <doi:10.1137/1126027> provides asymptotic distribution free test. This package provides test statistic and critical value of the test for normal, Cauchy, and logistic distributions. This package used the main algorithm proposed by Kim (2020) <doi:10.1007/s00180-020-00971-7> and tests for other distributions will be available at the later version.

Authors:Jiwoong Kim [aut, cre]

KMT_1.0.0.tar.gz
KMT_1.0.0.zip(r-4.7)KMT_1.0.0.zip(r-4.6)KMT_1.0.0.zip(r-4.5)
KMT_1.0.0.tgz(r-4.6-x86_64)KMT_1.0.0.tgz(r-4.6-arm64)KMT_1.0.0.tgz(r-4.5-x86_64)KMT_1.0.0.tgz(r-4.5-arm64)
KMT_1.0.0.tar.gz(r-4.7-arm64)KMT_1.0.0.tar.gz(r-4.7-x86_64)KMT_1.0.0.tar.gz(r-4.6-arm64)KMT_1.0.0.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html
card.svg |card.png
KMT/json (API)

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

Bug tracker:https://github.com/cranhaven/cranhaven.r-universe.dev/issues

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

On CRAN:

Conda:

archivedpackagesr-universeopenblascppopenmp

1.70 score 5 stars 196 downloads 7 exports 30 dependencies

Last updated from:ba17e6cae9 (on package/KMT). Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK176
linux-devel-x86_64OK147
source / vignettesOK201
linux-release-arm64OK147
linux-release-x86_64OK154
macos-release-arm64OK169
macos-release-x86_64OK259
macos-oldrel-arm64OK147
macos-oldrel-x86_64OK183
windows-develOK248
windows-releaseOK128
windows-oldrelOK123
wasm-releaseFAIL104

Exports:ADBMCvMDistr_InformationDrawUnzKSRun_KMT

Dependencies:clicodetoolscpp11digestfarverfuturefuture.applyggplot2globalsgluegtablegumbelisobandlabelinglifecyclelistenvnumDerivparallellyR6RColorBrewerRcppRcppArmadillorlangRsolnpS7scalestruncnormvctrsviridisLitewithr