Functions to 'numericise' 'R' objects (coerce to numeric
    objects), summarise 'MCMC' (Monte Carlo Markov Chain) samples and
    calculate deviance residuals as well as 'R' translations of some
    'BUGS' (Bayesian Using Gibbs Sampling), 'JAGS' (Just Another Gibbs
    Sampler), 'STAN' and 'TMB' (Template Model Builder) functions.

Contains additional miscellaneous steps for the 'recipes' package.
    These steps are useful, but doesn't have a good home in other 'recipes'
    packages or its extensions.

The basic use of this package is with 3 sequential functions. One to generate expected cell means and standard deviations, along with correlation and covariance matrices in the case of repeated measurements. This is followed by experiment simulation i number of times. Finally, power is calculated from the simulated data. Features that may be considered in the model are interaction, measure correlation and non-normal distributions.

Finding life outside the planet Earth several is the ultimate goal of an astrobiologist. Using known astronomical measurements and assumptions the probability of extraterrestrial life existence could be estimated. Equations such as the Drake equation (1961) as stated in the paper of Molina (2019) <arXiv:1912.01783>, Seager (2013) <https://www.space.com/22648-drake-equation-alien-life-seager.html> and Foucher et al, (2017) <doi:10.3390/life7040040> are included in the 'extraterrestrial' package.

Provides summary statistics of local geospatial features within a given geographic area. 
	It does so by calculating the area covered by a target geospatial feature (i.e. buildings, parks, lakes, etc.). 
	The geospatial features can be of any type of geospatial data, including point, polygon or line data.

Maximum likelihood estimation of an extended class of row-column (RC) association models for two-dimensional contingency tables, which are formulated by a condition of reduced rank on a matrix of extended association parameters; see Forcina (2019) <arXiv:1910.13848>. These parameters are defined by choosing the logit type for the row and column variables among four different options and a transformation derived from suitable divergence measures.