Gaston Quero <gastonquero@gmail.com> and Sebastian Simondi <sebastian.simondi@gmail.com>  , with
contributions from Victoria Bonnecarrere and Lucia Gutierrez
One haplotype is a combination of SNP
(Single Nucleotide Polymorphisms) within the QTL (Quantitative Trait Loci).
clusterhap groups together all individuals of a population with the same haplotype.
Each group contains individual with the same allele in each SNP,
whether or not missing data. Thus, clusterhap groups individuals,
that to be imputed, have a non-zero probability of having the same alleles
in the entire sequence of SNP's. Moreover, clusterhap calculates such
probability from relative frequencies.

Optimal Distance-Based Clustering for Multidimensional Data with
Sequential Constraint
c(person("Tibor", "Szkaliczki", role = c("aut", "cre"),
email = "szkaliczki.tibor@sztaki.mta.hu"),
person("J.", "Song", role = "ctb",
email = "joemsong@cs.nmsu.edu"))
Tibor Szkaliczki [aut, cre],
J. Song [ctb]
A dynamic programming algorithm for optimal clustering multidimensional data with sequential constraint. The algorithm minimizes the sum of squares of within-cluster distances. The sequential constraint allows only subsequent items of the input data to form a cluster. The sequential constraint is typically required in clustering data streams or items with time stamps such as video frames, GPS signals of a vehicle, movement data of a person, e-pen data, etc. The algorithm represents an extension of Ckmeans.1d.dp to multiple dimensional spaces. Similarly to the one-dimensional case, the algorithm guarantees optimality and repeatability of clustering. Method clustering.sc.dp can find the optimal clustering if the number of clusters is known. Otherwise, methods findwithinss.sc.dp and backtracking.sc.dp can be used.

Clustering is a central task in big data analyses and clusters are often Gaussian or near Gaussian. However, a flexible Gaussian cluster simulation tool with precise control over the size, variance, and spacing of the clusters in NXN dimensional space does not exist. This is why we created 'clusterlab'. The algorithm first creates X points equally spaced on the circumference of a circle in 2D space. These form the centers of each cluster to be simulated. Additional samples are added by adding Gaussian noise to each cluster center and concatenating the new sample co-ordinates. Then if the feature space is greater than 2D, the generated points are considered principal component scores and projected into N dimensional space using linear combinations using fixed eigenvectors. The algorithm is highly customizable and well suited to testing class discovery tools across a range of fields.