A 'hiredis' wrapper that includes support for
    transactions, pipelining, blocking subscription, serialisation of
    all keys and values, 'Redis' error handling with R errors.
    Includes an automatically generated 'R6' interface to the full
    'hiredis' API.  Generated functions are faithful to the
    'hiredis' documentation while attempting to match R's argument
    semantics.  Serialisation must be explicitly done by the user, but
    both binary and text-mode serialisation is supported.

A data mining approach for longitudinal and clustered data, 
        which combines the structure of mixed effects model with tree-based 
        estimation methods. See Sela, R.J. and Simonoff, J.S. (2012) RE-EM 
        trees: a data mining approach for longitudinal and clustered data 
        <doi:10.1007/s10994-011-5258-3>.

Implements an objective Bayes intrinsic conditional autoregressive 
    prior. This model provides an objective Bayesian approach for modeling spatially 
    correlated areal data using an intrinsic conditional autoregressive prior on a vector of 
    spatial random effects.

A robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance.

Imputation of missing numerical outcomes for a longitudinal trial 
    with protocol deviations.
    The package uses distinct treatment arm-based assumptions for the unobserved data, following 
    the general algorithm of Carpenter, Roger, and Kenward (2013) <doi:10.1080/10543406.2013.834911>, and the causal model
    of White, Royes and Best (2020) <doi:10.1080/10543406.2019.1684308>.
    Sensitivity analyses to departures from these assumptions can be done by the 
    Delta method of Roger.
    The program uses the same algorithm as the 'mimix' 'Stata' package written by Suzie Cro, 
    with additional coding for the causal model and delta method.
    The reference-based methods are jump to reference (J2R), 
    copy increments in reference (CIR), copy reference (CR),
    and the causal model, all of which must specify the reference treatment arm.
    Other methods are
    missing at random (MAR) and the last mean carried forward (LMCF). 
    Individual-specific imputation methods
    (and their reference groups) can be specified.