Converts files to and from flobs.  A flob is a file that was
    read into binary in integer-mode as little endian, saved as the single
    element of a named list (where the name is the name of the original
    file) and then serialized before being coerced into a blob.  Flobs are
    useful for writing and reading files to and from databases.

Implements synchronization between R processes (spawned by using the "parallel" package for instance) using file locks. Supports both exclusive and shared locking.

Fit occupancy models in 'Stan' via 'brms'. The full variety
    of 'brms' formula-based effects structures are available to use in
    multiple classes of occupancy model, including single-season
    models, models with data augmentation for never-observed species,
    dynamic (multiseason) models with explicit colonization and extinction
    processes, and dynamic models with autologistic occupancy dynamics.
    Formulas can be specified for all relevant distributional terms,
    including detection and one or more of occupancy, colonization,
    extinction, and autologistic depending on the model type. Several
    important forms of model post-processing are provided.  References:
    Bürkner (2017) <doi:10.18637/jss.v080.i01>; Carpenter et al. (2017)
    <doi:10.18637/jss.v076.i01>; Socolar & Mills (2023)
    <doi:10.1101/2023.10.26.564080>.

Includes several statistical methods for the estimation of parameters and high quantiles of river flow distributions. The focus is on regional estimation based on homogeneity assumptions and computed from multivariate observations (multiple measurement stations).
	For details see Kinsvater et al. (2017) <arXiv:1701.06455>.

Plotting flood quantiles and their corresponding probabilities (return periods) on the probability papers.
    The details of relevant methods are available in Chow et al (1988, ISBN: 007070242X, 9780070702424), and
    Bobee and Ashkar (1991, ISBN: 0918334683, 9780918334688).

Provides an efficient 'C++' code for computing an
 optimal segmentation model
 with Poisson loss,
 up-down constraints,
 and label constraints,
 as described by Kaufman et al. (2024) <doi:10.1080/10618600.2023.2293216>.