Package: zeitzeiger 2.1.3

Jake Hughey

zeitzeiger: Regularized Supervised Learning for Data from Rhythmic Systems

Method for predicting the value of a periodic variable from a high-dimensional observation. See Hughey et al. (2016) <doi:10.1093/nar/gkw030> and Hughey (2017) <doi:10.1186/s13073-017-0406-4>.

Authors:Jake Hughey [aut, cre]

# Install 'zeitzeiger' in R:

install.packages('zeitzeiger', repos = c('https://hugheylab.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Last updated 2 years agofrom:2b86199975. Checks:1 OK, 7 NOTE. Indexed: yes.

Last update: 2022-04-12
Started: 2015-11-21

Help page	Topics
Calculate circular difference	getCircDiff
Calculate time-dependent mean	predictIntensity
Train and test a ZeitZeiger predictor	zeitzeiger
Train and test a ZeitZeiger predictor, accounting for batch effects	zeitzeigerBatch
Combine predictions into an ensemble using the log-likelihood	zeitzeigerEnsembleLikelihood
Combine predictions into an ensemble using the circular mean	zeitzeigerEnsembleMean
Fit a periodic spline for each feature	zeitzeigerFit
Fit a periodic spline for each feature on cross-validation	zeitzeigerFitCv
Predict corresponding time for test observations	zeitzeigerPredict
Predict corresponding time for observations on cross-validation	zeitzeigerPredictCv
Predict corresponding time for groups of test observations	zeitzeigerPredictGroup
Predict corresponding time for groups of observations on cross-validation	zeitzeigerPredictGroupCv
Calculate sparse principal components of time-dependent variation	zeitzeigerSpc
Calculate sparse principal components of time-dependent variation on cross-validation	zeitzeigerSpcCv