Variable Selection and Inference for Partial Semiparametric Linear Mixed-Effects Model
Overview
Implements a partial linear semiparametric mixed-effects model (PLSMM) featuring a random intercept and applies a lasso penalty to both the fixed effects and the coefficients associated with the nonlinear function. The model also accommodates interactions between the nonlinear function and a grouping variable, allowing for the capture of group-specific nonlinearities. Nonlinear functions are modeled using a set of bases functions. Estimation is conducted using a penalized Expectation-Maximization algorithm, and the package offers flexibility in choosing between various information criteria for model selection. Post-selection inference is carried out using a debiasing method, while inference on the nonlinear functions employs a bootstrap approach.
plot_fit()andtest_f()plots now have an additional argumentshow_obsfor the scale of the x-axis.test_f()can be used even when the groups inname_group_varhave different timepoints.- The example in
tune_plsmm()was fixed. - Fixed some typos.
From NEWS.md in the release
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- OK13 OK2026-06-08
- ERROR12 OK, 1 ERROR2026-06-07
- OK13 OK2026-05-11
- WARNING12 OK, 1 WARNING2026-05-10
- OK14 OK2026-03-10
Documentation
- Help pages
- 8
- Examples that run
- 100%8 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
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- archivedRemoved from CRAN2026-07-10requires archived package 'scalreg'
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 1.1.02024-06-04 · diff ↗
- RR 4.4.0 released · 2024-04-24
- 1.0.02024-04-04
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2024-04-04
- Total releases
- 2 / 2 yrs
- License
- GPL (>= 3) OSI
- Download size
- 635 KB
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