MonteCarlo: MonteCarlo simulation for optimization
In roilanhv/longwaveR: Evaluate various downward longwave parametrizations

Make a MonteCarlo simulation from initial value on each schemes' coeficients. Initial sample has a 'nsample'*10 population number for each parameter in scheme, determined by a LHS (Latin Hypercubic Sample) with frontiers +- 5 times the value of coeficient: $$ A_i = [a_i-5a_i;a_i+5a_i] $$ The MonteCarlo design follows a similar way in genetic algorithms, where a initial population of value for parameter produce a L_i serie that is evaluated with choosen estatistic (stats). Always two equal dimenson population are compared and choosen combinations of parameters that produce a 'stats' minor than a o.5 quantile. That way ensures that best combinations be conserved if its performance is better than a half of total combinations 'stats' value. Finally a coeficients combination with best performance prevales and is considered the optimization. Furthermore, a 'nsample', 'max_iter' and 'stats' will define a result.

1	MonteCarlo(data, E_fun, func, coefs, nsample, max_iter, stats)

`data`	Data frame with all atmospherics variables
`E_fun`	Function for emissivity
`func`	Function from cloud cover calculation
`coefs`	Coeficients in E_fun function
`nsample`	Number of population individuous
`max_iter`	Maximun number of iterations
`stats`	Statistical index to be minimized