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.

Estimate population kin counts and its distribution by type, age and sex.
    The package implements one-sex and two-sex framework for studying living-death availability,
    with time varying rates or not, and multi-stage model.

Mimics the demo functionality for 'Shiny' apps in a package. Apps stored to the package subdirectory inst/shiny can be called by demoShiny(topic).

Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets.