rmargint
2.0.3Robust Marginal Integration Procedures
Overview
Three robust marginal integration procedures for additive models based on local polynomial kernel smoothers. As a preliminary estimator of the multivariate function for the marginal integration procedure, a first approach uses local constant M-estimators, a second one uses local polynomials of order 1 over all the components of covariates, and the third one uses M-estimators based on local polynomials but only in the direction of interest. For this last approach, estimators of the derivatives of the additive functions can be obtained. All three procedures can compute predictions for points outside the training set if desired. See Boente and Martinez (2017) doi:10.1007/s11749-016-0508-0 for details.
Install
Health
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Downloads
Dependencies
Nothing depends on this yet.
Code & Tests
- Cyclomatic complexity
- 1.0 median / 105 max
- Documented parameters
- 100%
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
74 10 exported
Complexity
7.3 avg / 105 max
Call network
74 nodes / 109 edges
Test coverage has not been measured for this package yet; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
People & History
3 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 2.0.3Latest
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 2.0.22020-08-05 · diff ↗
- RR 4.0.0 released · 2020-04-24
- 1.0.22019-06-28
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2019-06-28
- Total releases
- 3 / 7 yrs
- License
- GPL (>= 3.0)
- Download size
- 19 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet