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rdpeng
filehash:Simple Key-Value Database
Implements a simple key-value style database where character string keys are associated with data values that are stored on the disk. A simple interface is provided for inserting, retrieving, and deleting data from the database. Utilities are provided that allow 'filehash' databases to be treated much like environments and lists are already used in R. These utilities are provided to encourage interactive and exploratory analysis on large datasets. Three different file formats for representing the database are currently available and new formats can easily be incorporated by third parties for use in the 'filehash' framework.
Maintained by Roger D. Peng. Last updated 2 years ago.
24 stars 8.16 score 78 scripts 11 dependents
cfwp
rags2ridges:Ridge Estimation of Precision Matrices from High-Dimensional Data
Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) <doi:10.18637/jss.v102.i04> and associated publications.
Maintained by Carel F.W. Peeters. Last updated 1 years ago.
c-plus-plusgraphical-modelsmachine-learningnetworksciencestatisticsopenblascpp
8 stars 5.60 score 46 scripts
mirrelijn
ecpc:Flexible Co-Data Learning for High-Dimensional Prediction
Fit linear, logistic and Cox survival regression models penalised with adaptive multi-group ridge penalties. The multi-group penalties correspond to groups of covariates defined by (multiple) co-data sources. Group hyperparameters are estimated with an empirical Bayes method of moments, penalised with an extra level of hyper shrinkage. Various types of hyper shrinkage may be used for various co-data. Co-data may be continuous or categorical. The method accommodates inclusion of unpenalised covariates, posterior selection of covariates and multiple data types. The model fit is used to predict for new samples. The name 'ecpc' stands for Empirical Bayes, Co-data learnt, Prediction and Covariate selection. See Van Nee et al. (2020) <arXiv:2005.04010>.
Maintained by Mirrelijn M. van Nee. Last updated 2 years ago.
1.00 score 9 scripts