Functions to reconstruct sessions from web log or other user trace data
             and calculate various metrics around them, producing tabular,
             output that is compatible with 'dplyr' or 'data.table' centered processes.

A lightweight toolkit to validate new observations when computing
    their predictions with a predictive model. The validation process 
    consists of two steps: (1) record relevant statistics and meta data of the
    variables in the original training data for the predictive model and
    (2) use these data to run a set of basic validation tests on the new set of 
    observations.

Provides functions for linking and deduplicating data sets.
  Methods based on a stochastic approach are implemented as well as 
  classification algorithms from the machine learning domain. For details, 
  see our paper "The RecordLinkage Package: Detecting Errors in Data" 
  Sariyar M / Borg A (2010) <doi:10.32614/RJ-2010-017>.

Functions for generating k-record values and k-record
        times

Statistical tools based on the probabilistic properties of the 
  record occurrence in a sequence of independent and identically distributed 
  continuous random variables. In particular, tools to prepare a time series 
  as well as distribution-free trend and change-point tests and graphical 
  tools to study the record occurrence. Details about the implemented tools 
  can be found in Castillo-Mateo et al. (2023a) <doi:10.18637/jss.v106.i05> 
  and Castillo-Mateo et al. (2023b) <doi:10.1016/j.atmosres.2023.106934>.

R wrapper of the 'libmf' library
    <https://www.csie.ntu.edu.tw/~cjlin/libmf/> for recommender
    system using matrix factorization. It is typically used to
    approximate an incomplete matrix using the product of two
    matrices in a latent space. Other common names for this task
    include "collaborative filtering", "matrix completion",
    "matrix recovery", etc. High performance multi-core parallel
    computing is supported in this package.

Rectangle packing is a packing problem where rectangles are placed
    into a larger rectangular region (without overlapping) in order to maximise
    the use space.  Rectangles are packed using the skyline heuristic as discussed 
    in Lijun et al (2011) 'A Skyline-Based Heuristic for the 2D Rectangular Strip
    Packing Problem' <doi:10.1007/978-3-642-21827-9_29>.  A function is also 
    included for determining a good small-sized box for containing a given
    set of rectangles.