An open API service providing mapping between scientific papers and software projects that are mentioned in them.

Papers: 10.1371/journal.pone.0254178

https://doi.org/10.1371/journal.pone.0254178

Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques

Cited by: 3
Author(s): Colin Griesbach, Andreas Groll, Elisabeth Bergherr
Published: about 4 years ago

Software Mentions 6

cran: glmmLasso
Variable Selection for Generalized Linear Mixed Models by L1-Penalized Estimation
Papers that mentioned: 11
Very Likely Science (75)
cran: GMMBoost
Likelihood-Based Boosting for Generalized Mixed Models
Papers that mentioned: 2
Very Likely Science (83)
cran: JM
Joint Modeling of Longitudinal and Survival Data
Papers that mentioned: 132
Very Likely Science (85)
cran: lme4
Linear Mixed-Effects Models using 'Eigen' and S4
Papers that mentioned: 9,267
Very Likely Science (100)
cran: mboost
Model-Based Boosting
Papers that mentioned: 32
Very Likely Science (77)
pypi: PACE
Data Quality of Experimental Data
Papers that mentioned: 13,779
Very Likely Science (90)