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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.

Versions across snapshots

VersionRepositoryFileSize
1.39 rolling linux/jammy R-4.5 PartCensReg_1.39.tar.gz 90.3 KiB
1.39 rolling linux/noble R-4.5 PartCensReg_1.39.tar.gz 90.2 KiB
1.39 rolling source/ R- PartCensReg_1.39.tar.gz 17.4 KiB
1.39 latest linux/jammy R-4.5 PartCensReg_1.39.tar.gz 90.3 KiB
1.39 latest linux/noble R-4.5 PartCensReg_1.39.tar.gz 90.2 KiB
1.39 latest source/ R- PartCensReg_1.39.tar.gz 17.4 KiB
1.39 2026-04-26 source/ R- PartCensReg_1.39.tar.gz 17.4 KiB
1.39 2026-04-23 source/ R- PartCensReg_1.39.tar.gz 17.4 KiB
1.39 2026-04-09 windows/windows R-4.5 PartCensReg_1.39.zip 92.7 KiB
1.39 2025-04-20 source/ R- PartCensReg_1.39.tar.gz 17.4 KiB

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