The "Manual on Low-flow Estimation and Prediction" (Gustard & Demuth (2009, ISBN:978-92-63-11029-9)), published by the World Meteorological Organisation, gives a comprehensive summary on how to analyse stream flow data focusing on low-flows. This packages provides functions to compute the described statistics and produces plots similar to the ones in the manual.

An implementation of locally Gaussian distributions. It provides methods for
    implementing locally Gaussian multivariate density estimation, conditional density 
    estimation, various independence tests for iid and time series data, a test for conditional 
    independence and a test for financial contagion.

Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.

Spatial and spatio-temporal modelling of point patterns using the
    log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal,
    multivariate and aggregated point processes using Markov chain Monte Carlo. See Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2015) <doi:10.18637/jss.v063.i07>.

Implements Cumulative Sum (CUSUM) control charts specifically
 designed for monitoring processes following a Gamma distribution. Provides
 functions to estimate distribution parameters, simulate control limits, and
 apply cautious learning schemes for adaptive thresholding. It supports
 upward and downward monitoring with guaranteed performance evaluated via
 Monte Carlo simulations. It is useful for quality control applications in
 industries where data follows a Gamma distribution. Methods are based on
 Madrid-Alvarez et al. (2024) <doi:10.1002/qre.3464> and Madrid-Alvarez et
 al. (2024) <doi:10.1080/08982112.2024.2440368>.