Provides S3 generic methods and some default implementations
    for Bayesian analyses that generate Markov Chain Monte Carlo (MCMC)
    samples.  The purpose of 'universals' is to reduce package
    dependencies and conflicts.  The 'nlist' package implements many of
    the methods for its 'nlist' class.

Provides well-known techniques for detecting univariate
    outliers. Methods for handling skewed distributions are included.
    The Hidiroglou-Berthelot (1986) method for detecting outliers in
    ratios of historical data is also implemented. When available,
    survey weights can be incorporated in the detection process.

Pseudo-random number generation of 17 univariate distributions proposed by Demirtas. (2005) <DOI:10.22237/jmasm/1114907220>.

Bindings to system utilities found in most Unix systems such as
    POSIX functions which are not part of the Standard C Library.

Separate a data frame in two based on key columns. The function
 unjoin() provides an inside-out version of a nested data frame. This is used
 to identify duplication and normalize it (in the database sense) by linking
 two tables with the redundancy removed. This is a basic requirement for
 detecting topology within spatial structures that has motivated the need for
 this package as a building block for workflows within more applied projects.

Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

Tools for fitting and assessing Bayesian multilevel regression 
  models that account for unmeasured confounders.

Fast flattening of hierarchical data structures (e.g. JSON, XML)
             into data.frames with a flexible spec language.