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.

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.

Weighted-L2 FPOP Maidstone et al. (2017) <doi:10.1007/s11222-016-9636-3> and pDPA/FPSN Rigaill (2010) <arXiv:1004.0887> algorithm for detecting multiple changepoints in the mean of a vector. Also includes a few model selection functions using Lebarbier (2005) <doi:10.1016/j.sigpro.2004.11.012> and the 'capsushe' package.

A collection of functions to optimize portfolios and to analyze them from different points of view.

Returns the noncentrality parameter of the noncentral F
        distribution if probability of type I and type II error, degrees
        of freedom of the numerator and the denominator are given.  It
        may be useful for computing minimal detectable differences for
        general ANOVA models.  This program is documented in the paper
        of A. Baharev, S. Kemeny, On the computation of the noncentral
        F and noncentral beta distribution; Statistics and Computing,
        2008, 18 (3), 333-340.