Initializes a class that obtains API credentials and provides
    a method to use those credentials to make GET requests to the 'Hakai'
    API server. Usage instructions are documented at
    <https://hakaiinstitute.github.io/hakai-api/>.

A scalable implementation of the highly adaptive lasso algorithm,
  including routines for constructing sparse matrices of basis functions of the
  observed data, as well as a custom implementation of Lasso regression tailored
  to enhance efficiency when the matrix of predictors is composed exclusively of
  indicator functions. For ease of use and increased flexibility, the Lasso
  fitting routines invoke code from the 'glmnet' package by default. The highly
  adaptive lasso was first formulated and described by MJ van der Laan (2017)
  <doi:10.1515/ijb-2015-0097>, with practical demonstrations of its performance
  given by Benkeser and van der Laan (2016) <doi:10.1109/DSAA.2016.93>. This
  implementation of the highly adaptive lasso algorithm was described by Hejazi,
  Coyle, and van der Laan (2020) <doi:10.21105/joss.02526>.

An algorithm for flexible conditional density estimation based on
    application of pooled hazard regression to an artificial repeated measures
    dataset constructed by discretizing the support of the outcome variable. To
    facilitate flexible estimation of the conditional density, the highly
    adaptive lasso, a non-parametric regression function shown to estimate
    cadlag (RCLL) functions at a suitably fast convergence rate, is used. The
    use of pooled hazards regression for conditional density estimation as
    implemented here was first described for by Díaz and van der Laan (2011)
    <doi:10.2202/1557-4679.1356>. Building on the conditional density estimation
    utilities, non-parametric inverse probability weighted (IPW) estimators of
    the causal effects of additive modified treatment policies are implemented,
    using conditional density estimation to estimate the generalized propensity
    score. Non-parametric IPW estimators based on this can be coupled with
    undersmoothing of the generalized propensity score estimator to attain the
    semi-parametric efficiency bound (per Hejazi, Díaz, and van der Laan
    <doi:10.48550/arXiv.2205.05777>).