Building predictive models with stacking which is a type of ensemble learning. Learners can be specified from those implemented in 'caret'. For more information of the package, see Nukui and Onogi (2023) <doi:10.1101/2023.06.06.543970>.

Model stacking is an ensemble technique that involves
    training a model to combine the outputs of many diverse statistical
    models, and has been shown to improve predictive performance in a
    variety of settings. 'stacks' implements a grammar for
    'tidymodels'-aligned model stacking.

S4 class wrappers for the 'ODBC' and Pool DBI connection, also provides some 
    utilities to paste small datasets to clipboard, rename columns. It is used by the package 'stacomiR' for
    connections to the database. Development versions of 'stacomiR' are available in R-forge.

Wraps the 'rstac' package with a pipe-friendly, tidy API. All results return 'tibbles' instead of nested lists. Ships with a catalog registry of known 'STAC' endpoints including Planetary Computer, Earth Search, and 'USGS', while supporting any 'STAC' API URL.

Creates and fits staged event tree probability models,
             which are probabilistic graphical models capable of representing
             asymmetric conditional independence statements
             for categorical variables.
             Includes functions to create, plot and fit staged
             event trees from data, as well as many efficient structure
             learning algorithms.
             References:
             Carli F, Leonelli M, Riccomagno E, Varando G (2022).
             <doi: 10.18637/jss.v102.i06>.
             Collazo R. A., Görgen C. and Smith J. Q.
             (2018, ISBN:9781498729604).
             Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. (2018)
             <arXiv:1705.09457>.
             Thwaites P. A., Smith, J. Q. (2017) <arXiv:1510.00186>.
             Barclay L. M., Hutton J. L. and Smith J. Q. (2013)
             <doi:10.1016/j.ijar.2013.05.006>.
             Smith J. Q. and Anderson P. E. (2008)
             <doi:10.1016/j.artint.2007.05.004>.