An 'RStudio' Addin for Hippie Expand (AKA Hippie Code
  Completion or Cyclic Expand Word). This type of completion searches for 
  matching tokens within the user's current source editor file, regardless of
  file type. By searching only within the current source file, 'hippie' offers a
  fast way to identify and insert completions that appear around the user's
  cursor.

Read hierarchical fixed width files like those commonly used by 
    many census data providers. Also allows for reading of data in chunks,
    and reading 'gzipped' files without storing the full file in memory.

Identifying labeled compounds in a 13C-tracer experiment in
    non-targeted fashion is a cumbersome process. This package facilitates
    such type of analyses by providing high level quality control plots,
    deconvoluting and evaluating spectra and performing a multitude of
    tests in an automatic fashion.  The main idea is to use changing
    intensity ratios of ion pairs from peak list generated with 'xcms' as
    candidates and evaluate those against base peak chromatograms and
    spectra information within the raw measurement data automatically.
    The functionality is described in Hoffmann et al. (2018)
    <doi:10.1021/acs.analchem.8b00356>.

Implementation of a class of hierarchical item response
  theory (IRT) models where both the mean and the variance of latent preferences
  (ability parameters) may depend on observed covariates. The current
  implementation includes both the two-parameter latent trait model for binary data and the
  graded response model for ordinal data. Both are fitted via the Expectation-Maximization (EM)
  algorithm. Asymptotic standard errors are derived from the observed information
  matrix.

Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification. Beaulieu and O'Meara (2016) <doi:10.1093/sysbio/syw022>.

In some cases you will have data in a histogram format, where
  you have a vector of all possible observations, and a vector of how many
  times each observation appeared. You could expand this into a single 1D
  vector, but this may not be advisable if the counts are extremely large.
  'HistDat' allows for the calculation of summary statistics without the need
  for expanding your data.