Provides functions for summarizing and plotting the output of the command-line tool 'BeXY' (<https://bitbucket.org/wegmannlab/bexy>), a tool that performs Bayesian inference of sex chromosome karyotypes and sex-linked scaffolds from low-depth sequencing data.

Allows to compare the goodness of fit of Benford's and Blondeau Da Silva's digit distributions in a dataset. It is used to check whether the data distribution is consistent with theoretical distributions highlighted by Blondeau Da Silva or not (through the dat.distr() function): this ideal theoretical distribution must be at least approximately followed by the data for the use of Blondeau Da Silva's model to be well-founded. It also enables to plot histograms of digit distributions, both observed in the dataset and given by the two theoretical approaches (with the digit.ditr() function). Finally, it proposes to quantify the goodness of fit via Pearson's chi-squared test (with the chi2() function).

Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126>, A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et al (2023) <doi:10.48550/arXiv.2303.11561>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.

The bezier package is a toolkit for working with Bezier curves and splines. The package provides functions for point generation, arc length estimation, degree elevation and curve fitting.

Decomposition of time series into
    trend, seasonal, and remainder components with methods for detecting and
    characterizing abrupt changes within the trend and seasonal components. 'BFAST'
    can be used to analyze different types of satellite image time series and can
    be applied to other disciplines dealing with seasonal or non-seasonal time
    series, such as hydrology, climatology, and econometrics. The algorithm can be
    extended to label detected changes with information on the parameters of the
    fitted piecewise linear models. 'BFAST' monitoring functionality is described
    in Verbesselt et al. (2010) <doi:10.1016/j.rse.2009.08.014>. 'BFAST monitor'
    provides functionality to detect disturbance in near real-time based on 'BFAST'-
    type models, and is described in Verbesselt et al. (2012) <doi:10.1016/j.rse.2012.02.022>.
    'BFAST Lite' approach is a flexible approach that handles missing data
    without interpolation, and will be described in an upcoming paper.
    Furthermore, different models can now be used to fit the
    time series data and detect structural changes (breaks).