A set of tools for inspecting and understanding R data
    structures inspired by str(). Includes ast() for visualizing abstract
    syntax trees, ref() for showing shared references, cst() for showing
    call stack trees, and obj_size() for computing object sizes.

Network analysis usually requires estimating the uncertainty of
    graph statistics. Through this package, we provide tools to bootstrap 
    various networks via local bootstrap procedure. Additionally, it includes 
    functions for generating probability matrices, creating network adjacency 
    matrices from probability matrices, and plotting network structures. 
    The reference will be updated soon.

Implements novel nonparametric approaches to address
    biases and confounding when comparing treatments or exposures in
    observational studies of outcomes. While designed and appropriate for use
    in studies involving medicine and the life sciences, the package can be
    used in other situations involving outcomes with multiple confounders.
    The package implements a family of methods for non-parametric bias correction
    when comparing treatments in observational studies, including survival
    analysis settings, where competing risks and/or censoring may be present.
    The approach extends to bias-corrected personalized predictions of treatment
    outcome differences, and analysis of heterogeneity of treatment effect-sizes
    across patient subgroups. For further details, please see: 
    Lauve NR, Nelson SJ, Young SS, Obenchain RL, Lambert CG. LocalControl:
    An R Package for Comparative Safety and Effectiveness Research.
    Journal of Statistical Software. 2020. p. 1–32. Available from <doi:10.18637/jss.v096.i04>.

Implements a local likelihood estimator for the dependence parameter in bivariate conditional copula models.  Copula family and local likelihood bandwidth parameters are selected by leave-one-out cross-validation.  The models are implemented in 'TMB', meaning that the local score function is efficiently calculated via automated differentiation (AD), such that quasi-Newton algorithms may be used for parameter estimation.