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lgpr

Longitudinal Gaussian Process Regression

Interpretable nonparametric modeling of longitudinal data using additive Gaussian process regression. Contains functionality for inferring covariate effects and assessing covariate relevances. Models are specified using a convenient formula syntax, and can include shared, group-specific, non-stationary, heterogeneous and temporally uncertain effects. Bayesian inference for model parameters is performed using 'Stan'. The modeling approach and methods are described in detail in Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.

Versions across snapshots

VersionRepositoryFileSize
1.2.5 rolling linux/jammy R-4.5 lgpr_1.2.5.tar.gz 2.2 MiB
1.2.5 rolling linux/noble R-4.5 lgpr_1.2.5.tar.gz 2.2 MiB
1.2.5 rolling source/ R- lgpr_1.2.5.tar.gz 198.8 KiB
1.2.5 latest linux/jammy R-4.5 lgpr_1.2.5.tar.gz 2.2 MiB
1.2.5 latest linux/noble R-4.5 lgpr_1.2.5.tar.gz 2.2 MiB
1.2.5 latest source/ R- lgpr_1.2.5.tar.gz 198.8 KiB
1.2.5 2026-04-26 source/ R- lgpr_1.2.5.tar.gz 198.8 KiB
1.2.5 2026-04-23 source/ R- lgpr_1.2.5.tar.gz 198.8 KiB
1.2.5 2026-04-09 windows/windows R-4.5 lgpr_1.2.5.zip 2.4 MiB
1.2.4 2025-04-20 source/ R- lgpr_1.2.4.tar.gz 201.9 KiB

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