A flexible permutation framework for making 
    inference such as point estimation, confidence 
    intervals or hypothesis testing, on any kind of data, 
    be it univariate, multivariate, or more complex such 
    as network-valued data, topological data, functional 
    data or density-valued data.

Provides robust tests for testing in GLMs, by sign-flipping score contributions. The tests are robust against overdispersion, heteroscedasticity and, in some cases, ignored nuisance variables. See Hemerik, Goeman and Finos (2020) <doi:10.1111/rssb.12369>.

Lints are code patterns that are not optimal because they are
    inefficient, forget corner cases, or are less readable. 'flir' provides a
    small set of functions to detect those lints and automatically fix them.
    It builds on 'astgrepr', which itself uses the 'Rust' crate 'ast-grep' to
    parse and navigate R code.

R comes with a suite of utilities for linear algebra with "numeric"
    (double precision) vectors/matrices. However, sometimes single precision (or
    less!) is more than enough for a particular task.  This package extends R's
    linear algebra facilities to include 32-bit float (single precision) data.
    Float vectors/matrices have half the precision of their "numeric"-type
    counterparts but are generally faster to numerically operate on, for a
    performance vs accuracy trade-off.  The internal representation is an S4
    class, which allows us to keep the syntax identical to that of base R's.
    Interaction between floats and base types for binary operators is generally
    possible; in these cases, type promotion always defaults to the higher
    precision.  The package ships with copies of the single precision 'BLAS' and
    'LAPACK', which are automatically built in the event they are not available
    on the system.

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>.