Analysis of plant pathogen pathotype survey data.  Functions
    provided calculate distribution of susceptibilities, distribution of
    complexities with statistics, pathotype frequency distribution, as
    well as diversity indices for pathotypes.  This package is meant to be
    a direct replacement for Herrmann, Löwer and Schachtel's (1999)
    <doi:10.1046/j.1365-3059.1999.00325.x> Habgood-Gilmour Spreadsheet,
    'HaGiS', previously used for pathotype analysis.

Haplotype-aware Hidden Markov Model for RNA (HaHMMR) is a method for detecting copy number variations (CNVs) from bulk RNA-seq data. Additional examples, documentations, and details on the method are available at <https://github.com/kharchenkolab/hahmmr/>.

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 non/semi-parametric estimation of the conditional density, the
    highly adaptive lasso, a nonparametric regression function shown to reliably
    estimate a large class of functions at a fast convergence rate, is utilized.
    The pooled hazards data augmentation formulation implemented was first
    described by Díaz and van der Laan (2011) <doi:10.2202/1557-4679.1356>. To
    complement the conditional density estimation utilities, tools for efficient
    nonparametric inverse probability weighted (IPW) estimation of the causal
    effects of stochastic shift interventions (modified treatment policies),
    directly utilizing the density estimation technique for construction of the
    generalized propensity score, are provided. These IPW estimators utilize
    undersmoothing (sieve estimation) of the conditional density estimators in
    order to achieve the non/semi-parametric efficiency bound.