Construction and analysis of matrix population models in R.

Connects to the <https://genderize.io/>, <https://agify.io/> and <https://nationalize.io/> APIs to estimate gender, age and nationality of a first name.

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.

Functions for creating demographic table with simple
       summary statistics, with optional comparison(s) over one or
       more groups.  Numeric variables are summarized in means,
       standard deviations, medians, inter-quartile-ranges (IQR),
       skewness, Shapiro-Wilk normality test and ranges, and
       compared using two-sample t-test, Wilcoxon test, ANOVA and/or
       Kruskal-Wallis test. Logical and factor variables are
       summarized in counts and percentages and compared using
       chi-squared test and/or Fisher's exact test.

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.