Implements statistical inference for systems of ordinary differential equations,
  that uses the integral-matching criterion and takes advantage of the separability of parameters,
  in order to obtain initial parameter estimates for nonlinear least squares optimization. 
  Dattner & Yaari (2018) <arXiv:1807.04202>. 
  Dattner et al. (2017) <doi:10.1098/rsif.2016.0525>.
  Dattner & Klaassen (2015) <doi:10.1214/15-EJS1053>.

Develop spatial interaction models (SIMs).  SIMs predict the
    amount of interaction, for example number of trips per day, between
    geographic entities representing trip origins and destinations.
    Contains functions for creating origin-destination datasets
    from geographic input datasets and calculating movement between
    origin-destination pairs with constrained, production-constrained,
    and attraction-constrained models (Wilson 1979) <doi:10.1068/a030001>.

Simulates and plots quantities of interest (relative
    hazards, first differences, and hazard ratios) for linear coefficients,
    multiplicative interactions, polynomials, penalised splines, and
    non-proportional hazards, as well as stratified survival curves from Cox
    Proportional Hazard models. It also simulates and plots marginal effects
    for multiplicative interactions. Methods described in Gandrud (2015)
    <doi:10.18637/jss.v065.i03>.

A tool for simulating rhythmic data: transcriptome data using
  Gaussian or negative binomial distributions, and behavioral activity data
  using Bernoulli or Poisson distributions. See Singer et al. (2019)
  <doi:10.7717/peerj.6985>.

Interface to interact with the modelling framework 'SIMPLACE' and to
    parse the results of simulations.

A simple function that anonymises a list of variables
    in a consistent way: anonymised factors are not recycled and
    the same original levels receive the same anonymised factor
    even if located in different datasets.

Provides SPSS- and SAS-like output for least squares multiple 
    regression and moderated regression, as well as interaction plots and 
    Johnson-Neyman regions of significance for interactions. The output 
    includes standardized coefficients, partial and semi-partial correlations, 
    collinearity diagnostics, plots of residuals, and detailed information 
    about simple slopes for interactions. There are numerous options for 
    designing interaction plots, including plots of interactions for both lm
    and lme models.