Provides methods for inference using stacked multiple 
    imputations augmented with weights. The vignette provides example R code for 
    implementation in general multiple imputation settings. For additional details 
    about the estimation algorithm, we refer the reader to Beesley, Lauren J and 
    Taylor, Jeremy M G (2020) “A stacked approach for chained equations multiple 
    imputation incorporating the substantive model” <doi:10.1111/biom.13372>, 
    and Beesley, Lauren J and Taylor, Jeremy M G (2021) “Accounting for not-at-random 
    missingness through imputation stacking” <arXiv:2101.07954>.

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.

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>.