Functions for detecting and analyzing rare events in data.
    Implements isolation forest (Liu et al., 2008, <doi:10.1109/ICDM.2008.17>)
    and clustering
    for anomaly detection in time series residuals. Decomposes time series
    using LOESS (Locally Estimated Scatterplot Smoothing) or STL 
    (Seasonal-Trend decomposition using LOESS). Detects marine heatwaves 
    and cold spells following Hobday et al. (2016) <doi:10.1016/j.pocean.2015.12.014>.
    Provides goodness-of-fit tests for quantile regression (Haupt et al.,
    2011, <doi:10.1080/02664763.2011.573542>), partial dependence with
    quantile random forests, MCC (Matthews Correlation Coefficient)
    computation and testing, knee-point detection via the Kneedle algorithm
    (Satopaa et al., 2011, <doi:10.1109/ICDCSW.2011.20>), and spatial
    point matching.

Randomization tests for the statistical comparison of i = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the i samples were all drawn randomly from a single assemblage, with (necessarily) a single underlying species abundance distribution. The biogeographic null hypothesis is that the i samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions. Functions are described in L. Cayuela, N.J. Gotelli & R.K. Colwell (2015) <doi:10.1890/14-1261.1>.

Calculate rarefaction-based alpha- and beta-diversity. Offer parametric extrapolation to estimate the total expected species in a single community and the total expected shared species between two communities. Visualize the curve-fitting for these estimators.

Provides functions to correct biased transition and fertility
    estimates in population projection matrices caused by small sample sizes.
    Small or short-term studies frequently produce structural zeros
    (biologically possible transitions never observed) and structural ones
    (transitions estimated at 100% survival, stasis, or mortality that are
    biologically implausible). Both distort matrix structure and bias
    estimates of population growth. Implements a multinomial-Dirichlet
    Bayesian prior for transition probabilities and a Gamma-Poisson prior
    for reproduction, allowing analysts to incorporate prior biological
    knowledge and regularise estimates from rare or unobserved events.
    Includes functions to compute marginal posterior credible intervals for
    all transition probabilities (transition_CrI()), visualise those
    intervals as point-range plots (plot_transition_CrI()), and display
    the full posterior beta density for each matrix entry
    (plot_transition_density()). Methods are described in Tremblay et al.
    (2021) <doi:10.1016/j.ecolmodel.2021.109526>.