Easy way to plot regular/weighted/conditional distributions by using formulas. The core of the package concerns distribution plots which are automatic: the many options are tailored to the data at hand to offer the nicest and most meaningful graphs possible -- with no/minimum user input. Further provide functions to plot conditional trends and box plots. See <https://lrberge.github.io/fplot/> for more information.

Read and process a large delimited file block by
    block. A block consists of all the contiguous rows that have the same value
    in the first field. The result can be returned as a list or a data.table,
    or even directly printed to an output file.

Read 'FPOD' and 'CPOD' data into 'R' directly from the
    'FPOD' data files (i.e. .CP1, .CP3, .FP1 and .FP3 files). The 'FPOD' data
    files contain binary data, so they can't trivially be read into 'R' using the
    usual approach, e.g. fread() or read.csv(). This package decodes the binary
    data and imports all the data in one go (i.e. header/metadata, clicks, 
    'KERNO' classifications, environmental data and pseudo-WAV data). It is then 
    trivial to aggregate data as you please, e.g. detection-positive-minutes per
    time block. The advantage of handling data processing in 'R' is a long topic, 
    but suffice it to say that it 1) simplifies things (many fewer steps, as 
    different vars have to be exported in multiple goes in the official 'FPOD'
    app), and more importantly, 2) makes data processing transparent and 
    reproducible.
    References: Pirotta et al. 2014 <doi:10.1111/1365-2435.12146>.

A dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles.
  The 'fpop'  package is a wrapper to a C++ implementation of the fpop (Functional Pruning Optimal Partioning) algorithm described in Maidstone et al. 2017
  <doi:10.1007/s11222-016-9636-3>. The problem of detecting changepoints in an univariate sequence is formulated 
  in terms of minimising the mean squared error over segmentations. The fpop algorithm exactly minimizes the mean squared error 
  for a penalty linear in the number of changepoints.