Comparisons of floating point numbers are problematic due to errors
    associated with the binary representation of decimal numbers.
    Despite being aware of these problems, people still use numerical methods
    that fail to account for these and other rounding errors (this pitfall is
    the first to be highlighted in Circle 1 of Burns (2012)
    'The R Inferno' <https://www.burns-stat.com/pages/Tutor/R_inferno.pdf>).
    This package provides new relational operators useful for performing
    floating point number comparisons with a set tolerance.

Probabilistic distance clustering (PD-clustering) is an iterative, distribution-free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership under the constraint that the product of the probability and the distance of each point to any cluster center is a constant. PD-clustering is a flexible method that can be used with elliptical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different sizes. GPDC and TPDC use a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high-dimensional data sets.

Tools to help text files importation. It can return the
    number of lines; print the first and last lines; convert encoding;
    guess delimiters and file encoding. Operations are made without
    reading the entire file before starting, resulting in good
    performances with large files. This package provides an alternative to
    a simple use of the 'head', 'tail', 'wc' and 'iconv' programs that are
    not always available on machine where R is installed.

Given the values of sampled units and selection probabilities 
             the desraj function  in the package computes the estimated value of the total
             as well as estimated variance.

This data contains a large variety of information on players and their
  current attributes on Fantasy Premier League
  <https://fantasy.premierleague.com/>. In particular, it contains a
  `next_gw_points` (next gameweek points) value for each player
  given their attributes in the current week. Rows represent player-gameweeks,
  i.e. for each player there is a row for each gameweek. This
  makes the data suitable for modelling a player's next gameweek points, given
  attributes such as form, total points, and cost at the current gameweek.
  This data can therefore be used to create Fantasy Premier League bots that
  may use a machine learning algorithm and a linear programming solver
  (for example) to return the best possible transfers and team to pick for
  each gameweek, thereby fully automating the decision making process in
  Fantasy Premier League. This function simply supplies the required data
  for such a task.