Author: Cristina Tortora [aut, cre, cph], Noe Vidales [aut], Francesco Palumbo [aut], and Paul D. McNicholas [fnd]
Description: 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 centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different size. GPDC and TPDC uses a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a recently proposed factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high dimensional datasets.

Author: Cristina Tortora [aut, cre, cph], Noe Vidales [aut], Francesco Palumbo [aut], Tina Kalra [aut], and Paul D. McNicholas [fnd]
Description: 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 centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different size. GPDC and TPDC uses 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.
Imports: ExPosition,cluster,rootSolve, MASS, klaR, GGally, ggplot2

Title: Read in Fantasy Premier League Data
Description: 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
Packaged: 2021-12-02 14:08:50 UTC; andrew.little