Extends the capabilities for flexible partitioning and model-based clustering
       available in the packages 'flexclust' and 'flexmix' to handle ordinal and
       mixed-with-ordinal data types via new distance, centroid and driver functions that
       make various assumptions regarding ordinality. Using them within the flex-scheme
       allows for easy comparisons across methods.

Model selection tools and 'selfStart' functions to fit parametric curves in 'nls', 'nlsList' and 'nlme' frameworks.

Binding to the C++ implementation of the flexible polyline
    encoding by HERE <https://github.com/heremaps/flexible-polyline>. The
    flexible polyline encoding is a lossy compressed representation of a list of
    coordinate pairs or coordinate triples. The encoding is achieved by:
    (1) Reducing the decimal digits of each value;
    (2) encoding only the offset from the previous point;
    (3) using variable length for each coordinate delta; and
    (4) using 64 URL-safe characters to display the result.

Functions to fit regression models for bounded continuous and discrete responses. In case of bounded continuous  responses (e.g., proportions and rates), available models are the flexible beta (Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) <doi:10.1214/17-BA1079>), the variance-inflated beta (Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020) <doi:10.1177/1471082X18821213>), the beta (Ferrari, S.L.P., Cribari-Neto, F. (2004) <doi:10.1080/0266476042000214501>), and their augmented versions to handle the presence of zero/one values (Di Brisco, A. M., Migliorati, S. (2020) <doi:10.1002/sim.8406>) are implemented. In case of bounded discrete responses (e.g., bounded counts, such as the number of successes in n trials),  available models are the flexible beta-binomial (Ascari, R., Migliorati, S. (2021) <doi:10.1002/sim.9005>), the beta-binomial, and the binomial are implemented. Inference is dealt with a Bayesian approach based on the Hamiltonian Monte Carlo (HMC) algorithm (Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2014) <doi:10.1201/b16018>). Besides, functions to compute residuals, posterior predictives, goodness of fit measures, convergence diagnostics, and graphical representations are provided.