PLRModels
Statistical Inference in Partial Linear Regression Models
Contains statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. Kernel-based methods, combined with ordinary least squares estimation, are used and time series errors are allowed. In addition, these techniques are also implemented for both parametric (linear) and nonparametric regression models.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.4 |
rolling linux/jammy R-4.5 | PLRModels_1.4.tar.gz |
291.7 KiB |
1.4 |
rolling linux/noble R-4.5 | PLRModels_1.4.tar.gz |
291.6 KiB |
1.4 |
rolling source/ R- | PLRModels_1.4.tar.gz |
41.2 KiB |
1.4 |
latest linux/jammy R-4.5 | PLRModels_1.4.tar.gz |
291.7 KiB |
1.4 |
latest linux/noble R-4.5 | PLRModels_1.4.tar.gz |
291.6 KiB |
1.4 |
latest source/ R- | PLRModels_1.4.tar.gz |
41.2 KiB |
1.4 |
2026-04-26 source/ R- | PLRModels_1.4.tar.gz |
41.2 KiB |
1.4 |
2026-04-23 source/ R- | PLRModels_1.4.tar.gz |
41.2 KiB |
1.4 |
2026-04-09 windows/windows R-4.5 | PLRModels_1.4.zip |
296.0 KiB |
1.4 |
2025-04-20 source/ R- | PLRModels_1.4.tar.gz |
41.2 KiB |