Package: PartCensReg 1.39
PartCensReg: Estimation and Diagnostics for Partially Linear Censored Regression Models Based on Heavy-Tailed Distributions
It estimates the parameters of a partially linear regression censored model via maximum penalized likelihood through of ECME algorithm. The model belong to the semiparametric class, that including a parametric and nonparametric component. The error term considered belongs to the scale-mixture of normal (SMN) distribution, that includes well-known heavy tails distributions as the Student-t distribution, among others. To examine the performance of the fitted model, case-deletion and local influence techniques are provided to show its robust aspect against outlying and influential observations. This work is based in Ferreira, C. S., & Paula, G. A. (2017) <doi:10.1080/02664763.2016.1267124> but considering the SMN family.
Authors:
PartCensReg_1.39.tar.gz
PartCensReg_1.39.zip(r-4.5)PartCensReg_1.39.zip(r-4.4)PartCensReg_1.39.zip(r-4.3)
PartCensReg_1.39.tgz(r-4.4-any)PartCensReg_1.39.tgz(r-4.3-any)
PartCensReg_1.39.tar.gz(r-4.5-noble)PartCensReg_1.39.tar.gz(r-4.4-noble)
PartCensReg_1.39.tgz(r-4.4-emscripten)PartCensReg_1.39.tgz(r-4.3-emscripten)
PartCensReg.pdf |PartCensReg.html✨
PartCensReg/json (API)
| # Install 'PartCensReg' in R: |
| install.packages('PartCensReg', repos = c('https://mnunez5.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:e0752c6876. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 18 2024 |
| R-4.5-win | OK | Nov 18 2024 |
| R-4.5-linux | OK | Nov 18 2024 |
| R-4.4-win | OK | Nov 18 2024 |
| R-4.4-mac | OK | Nov 18 2024 |
| R-4.3-win | OK | Nov 18 2024 |
| R-4.3-mac | OK | Nov 18 2024 |
Exports:Cens.SMN.PCR
Dependencies:FormulaGIGrvglatticeMatrixnloptrnormalpnumDerivoptimxpracmasandwichssymsurvivalzoo
