Computation of various Markovian models for categorical data
    including homogeneous Markov chains of any order, MTD models, Hidden Markov
    models, and Double Chain Markov Models.

A set of tools for likelihood-based estimation, model selection and testing of two- and three-range shift and migration models for animal movement data as described in Gurarie et al. (2017) <doi: 10.1111/1365-2656.12674>.  Provided movement data (X, Y and Time), including irregularly sampled data, functions estimate the time, duration and location of one or two range shifts, as well as the ranging area and auto-correlation structure of the movment.  Tests assess, for example, whether the shift was "significant", and whether a two-shift migration was a true return migration.

Datasets, constants, conversion factors, and utilities for 'MArine', 'Riverine',
             'Estuarine', 'LAcustrine' and 'Coastal' science. 
             The package contains among others: (1) chemical and
             physical constants and datasets, e.g. atomic weights, gas
             constants, the earths bathymetry; (2) conversion factors
             (e.g. gram to mol to liter, barometric units,
             temperature, salinity); (3) physical functions, e.g. to
             estimate concentrations of conservative substances, gas
             transfer and diffusion coefficients, the Coriolis force
             and gravity; (4) thermophysical properties of the
             seawater, as from the UNESCO polynomial or from the more
             recent derivation based on a Gibbs function.

Local recombination rates are graphically estimated across a genome using Marey maps.

Likelihood inference based on higher order approximations 
  for linear nonnormal regression models.

The target of 'margaret' is help to extract data from Minciencias to analyze scientific production in Colombia.

Fits joint marginal conditional models for multivariate longitudinal data, as in Proudfoot, Faig, Natarajan, and Xu (2018) <doi:10.1002/sim.7552>. Development of this package was supported by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.