References and cites 'R' and 'R' packages on the fly in 'R
    Markdown' and 'Quarto'. 'pakret' provides a minimalist API that
    generates preformatted citations for 'R' and 'R' packages, and adds
    their references to a '.bib' file directly from within your document.

Several tests of quantitative palaeoenvironmental reconstructions 
  from microfossil assemblages, including the null model tests of the 
  statistically significant of reconstructions developed by Telford and Birks
  (2011) <doi:10.1016/j.quascirev.2011.03.002>, and tests of the effect of 
  spatial autocorrelation on transfer function model performance using methods 
  from Telford and Birks (2009) <doi:10.1016/j.quascirev.2008.12.020> and 
  Trachsel and Telford (2016) <doi:10.5194/cp-12-1215-2016>. Age-depth models with 
  generalized mixed-effect regression from Heegaard et al (2005)
  <doi:10.1191/0959683605hl836rr> are also included.

Provides functionality to support data preparation and exploration for
  palaeobiological analyses, improving code reproducibility and accessibility. The
  wider aim of 'palaeoverse' is to bring the palaeobiological community together 
  to establish agreed standards. The package currently includes functionality for 
  data cleaning, binning (time and space), exploration, summarisation and 
  visualisation. Reference datasets (i.e. Geological Time Scales <https://stratigraphy.org/chart>)
  and auxiliary functions are also provided. Details can be found in:
  Jones et al., (2023) <doi: 10.1111/2041-210X.14099>.

Implements sparse regression with paired covariates (<doi:10.1007/s11634-019-00375-6>). The paired lasso is designed for settings where each covariate in one set forms a pair with a covariate in the other set (one-to-one correspondence). For the optional correlation shrinkage, install ashr (<https://github.com/stephens999/ashr>) and CorShrink (<https://github.com/kkdey/CorShrink>) from GitHub (see README).

Implementation of the Partitioned Local Depth (PaLD) 
    approach which provides a measure of local depth and the cohesion of a point 
    to another which  (together with a universal threshold for distinguishing 
    strong and weak ties) may be used to reveal local and global structure in 
    data, based on methods described in Berenhaut, Moore, and Melvin (2022)
    <doi:10.1073/pnas.2003634119>. No extraneous inputs, distributional 
    assumptions, iterative procedures nor optimization criteria are employed. 
    This package includes functions for computing local depths and cohesion as
    well as flexible functions for plotting community networks and displays of 
    cohesion against distance.