Package: ARCensReg 3.0.2

ARCensReg: Fitting Univariate Censored Linear Regression Model with Autoregressive Errors

It fits a univariate left, right, or interval censored linear regression model with autoregressive errors, considering the normal or the Student-t distribution for the innovations. It provides estimates and standard errors of the parameters, predicts future observations, and supports missing values on the dependent variable. References used for this package: Schumacher, F. L., Lachos, V. H., & Dey, D. K. (2017). Censored regression models with autoregressive errors: A likelihood-based perspective. Canadian Journal of Statistics, 45(4), 375-392 <doi:10.1002/cjs.11338>. Schumacher, F. L., Lachos, V. H., Vilca-Labra, F. E., & Castro, L. M. (2018). Influence diagnostics for censored regression models with autoregressive errors. Australian & New Zealand Journal of Statistics, 60(2), 209-229 <doi:10.1111/anzs.12229>. Valeriano, K. A., Schumacher, F. L., Galarza, C. E., & Matos, L. A. (2024). Censored autoregressive regression models with Student‐t innovations. Canadian Journal of Statistics, 52(3), 804-828 <doi:10.1002/cjs.11804>.

Authors:Fernanda L. Schumacher [aut, cre], Katherine A. L. Valeriano [ctb], Victor H. Lachos [ctb], Christian G. Morales [ctb], Larissa A. Matos [ctb]

ARCensReg_3.0.2.tar.gz
ARCensReg_3.0.2.zip(r-4.7-x86_64)ARCensReg_3.0.2.zip(r-4.7-arm64)ARCensReg_3.0.2.zip(r-4.6-x86_64)ARCensReg_3.0.2.zip(r-4.6-arm64)ARCensReg_3.0.2.zip(r-4.5-x86_64)
ARCensReg_3.0.2.tgz(r-4.6-x86_64)ARCensReg_3.0.2.tgz(r-4.6-arm64)ARCensReg_3.0.2.tgz(r-4.5-x86_64)ARCensReg_3.0.2.tgz(r-4.5-arm64)
ARCensReg_3.0.2.tar.gz(r-4.7-arm64)ARCensReg_3.0.2.tar.gz(r-4.7-x86_64)ARCensReg_3.0.2.tar.gz(r-4.6-arm64)ARCensReg_3.0.2.tar.gz(r-4.6-x86_64)
ARCensReg_3.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ARCensReg/json (API)

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

Bug tracker:https://github.com/fernandalschumacher/arcensreg/issues

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

On CRAN:

Conda:

openblascppopenmp

2.70 score 1 stars 9 scripts 483 downloads 4 exports 57 dependencies

Last updated from:426ecc6a3e. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK178
linux-devel-x86_64OK241
source / vignettesOK215
linux-release-arm64OK165
linux-release-x86_64OK210
macos-release-arm64OK144
macos-release-x86_64OK217
macos-oldrel-arm64OK113
macos-oldrel-x86_64OK301
windows-devel-arm64OK425
windows-devel-x86_64OK179
windows-release-arm64OK390
windows-release-x86_64OK134
windows-oldrel-x86_64OK154
wasm-releaseOK137

Exports:ARCensRegARtCensRegInfDiagrARCens

Dependencies:bitopscaToolsclicodetoolscpp11DEoptimRdoParalleldplyrexpmfarverforeachgenericsggplot2gluegmmgridExtragtableisobanditeratorslabelinglatticelifecyclemagrittrMASSMatrixmatrixcalcmsmmvtnormnumDerivopdisDownsamplingpbmcapplypillarpkgconfigpracmaqqconfqqplotrR6rbibutilsRColorBrewerRcppRcppArmadilloRdpackrlangrobustbaseS7sandwichscalessurvivaltibbletidyselecttmvtnormtwosamplesutf8vctrsviridisLitewithrzoo