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FlexGAM

Generalized Additive Models with Flexible Response Functions

v0.7.2 · Jun 7, 2020 · GPL-2

Description

Standard generalized additive models assume a response function, which induces an assumption on the shape of the distribution of the response. However, miss-specifying the response function results in biased estimates. Therefore in Spiegel et al. (2017) <doi:10.1007/s11222-017-9799-6> we propose to estimate the response function jointly with the covariate effects. This package provides the underlying functions to estimate these generalized additive models with flexible response functions. The estimation is based on an iterative algorithm. In the outer loop the response function is estimated, while in the inner loop the covariate effects are determined. For the response function a strictly monotone P-spline is used while the covariate effects are estimated based on a modified Fisher-Scoring algorithm. Overall the estimation relies on the 'mgcv'-package.

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  'LazyData' is specified without a 'data' directory
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LazyData

  'LazyData' is specified without a 'data' directory
NOTE r-oldrel-windows-x86_64

LazyData

  'LazyData' is specified without a 'data' directory
OK r-patched-linux-x86_64

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OK r-release-linux-x86_64

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OK r-release-macos-arm64

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Check History

NOTE 11 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-oldrel-macos-arm64

LazyData

  'LazyData' is specified without a 'data' directory
NOTE r-oldrel-macos-x86_64

LazyData

  'LazyData' is specified without a 'data' directory
NOTE r-oldrel-windows-x86_64

LazyData

  'LazyData' is specified without a 'data' directory

Dependency Network

Dependencies Reverse dependencies mgcv MASS Matrix scam FlexGAM

Version History

new 0.7.2 Mar 9, 2026