Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, identification keys (dichotomous and interactive), and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology histograms and distributional and diversity maps are also available.

'Monolix' is a tool for running mixed effects model using
    'saem'. This tool allows you to convert 'Monolix' models to 'rxode2'
    (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) using the
    form compatible with 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>).
    If available, the 'rxode2' model will
    read in the 'Monolix' data and compare the simulation for the
    population model individual model and residual
    model to immediately show how well the translation is
    performing. This saves the model development time for people who are
    creating an 'rxode2' model manually.  Additionally, this package reads
    in all the information to allow simulation with uncertainty (that is the
    number of observations, the number of subjects, and the covariance
    matrix) with a 'rxode2' model.  This is complementary to the
    'babelmixr2' package that translates 'nlmixr2' models to 'Monolix' and can
    convert the objects converted from 'monolix2rx' to a full 'nlmixr2' fit. While not
    required, you can  get/install the 'lixoftConnectors' package in the 'Monolix'
    installation, as described at the following url   <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When
  'lixoftConnectors' is available, 'Monolix' can be used to load its model library
  instead manually setting up text files (which only works with old versions of
  'Monolix').

Estimation of multivariate normal (MVN) and student-t data of 
 arbitrary dimension where the pattern of missing data is monotone.
 See Pantaleo and Gramacy (2010) <doi:10.48550/arXiv.0907.2135>.
 Through the use of parsimonious/shrinkage regressions 
 (plsr, pcr, lasso, ridge,  etc.), where standard regressions fail, 
 the package can handle a nearly arbitrary amount of missing data. 
 The current version supports maximum likelihood inference and 
 a full Bayesian approach employing scale-mixtures for Gibbs sampling.
 Monotone data augmentation extends this Bayesian approach to arbitrary 
 missingness patterns.  A fully functional standalone interface to the 
 Bayesian lasso (from Park & Casella), Normal-Gamma (from Griffin & Brown),
 Horseshoe (from Carvalho, Polson, & Scott), and ridge regression 
 with model selection via Reversible Jump, and student-t errors 
 (from Geweke) is also provided.