Designed to support the visualization, numerical computation,
     qualitative analysis, model-data fusion, and stochastic simulation for
     autonomous systems of differential equations. Euler and Runge-Kutta methods
     are implemented, along with tools to visualize the two-dimensional
     phaseplane. Likelihood surfaces and a simple Markov Chain Monte Carlo
     parameter estimator can be used for model-data fusion of differential
     equations and empirical models. The Euler-Maruyama method is provided for
     simulation of stochastic differential equations. The package was originally
     written for internal use to support teaching by Zobitz, and refined to
     support the text "Exploring modeling with data and differential equations
     using R" by John Zobitz (2021) <https://jmzobitz.github.io/ModelingWithR/index.html>.

Construction and analysis of matrix population models in R.

The Demographic Table in R combines contingency table for categorical variables, mean and standard deviation for continuous variables. t-test, chi-square test and Fisher's exact test calculated the p-value of two groups. The standardized mean difference were performed with 95 % confident interval, and writing table into document file.

To create demographic table with simple summary statistics,
        with optional comparison(s) over one or more groups.

Functions for demographic analysis including lifetable
        calculations; Lee-Carter modelling; functional data analysis of
        mortality rates, fertility rates, net migration numbers; and
        stochastic population forecasting.

Demonstration code showing how (univariate) kernel
	     density estimates are computed, at least conceptually,
	     and allowing users to experiment with different kernels,
	     should they so wish.  The method used follows directly
	     the definition, but gains efficiency by replacing the
	     observations by frequencies in a very fine grid covering
	     the sample range.  A canonical reference is
	     B. W. Silverman, (1998) <doi: 10.1201/9781315140919>.
	     NOTE: the density function in the
	     stats package uses a more sophisticated method based on the
	     fast Fourier transform and that function should be used if
	     computational efficiency is a prime consideration.