Provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel-coding method (only simple indel-coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and collapsing identical DNA sequences into haplotypes or inferring haplotypes using user-provided absolute pairwise character difference matrix.  This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony and plotting parsimony networks.

A local haplotyping tool for use in trait association and trait prediction analyses pipelines.
    'HaploVar' enables users take single nucleotide polymorphisms (SNPs) (in VCF 
    format) and a linkage disequilibrium (LD) matrix, calculate local haplotypes and format the 
    output to be compatible with a wide range of trait association and trait prediction tools. 
    The local haplotypes are calculated from the LD matrix using a clustering algorithm called 
    density-based spatial clustering of applications with noise ('DBSCAN') (Ester et al., 1996) 
    <ISBN: 1577350049>.

Automatic open data acquisition from resources of IGN
    ('Institut National de Information Geographique et forestiere')
    (<https://www.ign.fr/>). Available datasets include various types of
    raster and vector data, such as digital elevation models, state
    borders, spatial databases, cadastral parcels, and more. 'happign' also 
    provide access to API Carto (<https://apicarto.ign.fr/api/doc/>).

Simulate populations and track haplotypes over generations to evaluate population admixture. The 'HAPTRACE' supports customisable population parameters, including size, number of markers, mutation rates and recombination.

By analyzing time series, it is possible to observe significant changes in the behavior of observations that frequently characterize events. Events present themselves as anomalies, change points, or motifs. In the literature, there are several methods for detecting events. However, searching for a suitable time series method is a complex task, especially considering that the nature of events is often unknown. This work presents Harbinger, a framework for integrating and analyzing event detection methods. Harbinger contains several state-of-the-art methods described in Salles et al. (2020) <doi:10.5753/sbbd.2020.13626>.