do.plain.mc: Do plain monte carlo with target density
In optismixture: Optimal Mixture Weights in Multiple Importance Sampling

Do plain monte carlo with target density

1	do.plain.mc(plainmc.N, mixture.param, fname = "f", rpname = "rp")

`plainmc.N`	number of samples
`mixture.param`	mixture.param = list(p, J, ...), where p is the dimension of the sample, and J is the number of mixture components, including the defensive one. mixture.param should be compatible with user defined functions f(n, j, mixture.param), rp(n, mixture.param), rq(n, j, mixture.param), dp(xmat, mixture.param), dq(xmat, j, mixture.param)
`fname`	name of user defined function fname(xmat, j, mixture.param). xmat is an n \times p matrix of n samples with p dimensions. fname returns a vector of function values for each row in xmat. fname is defined for j = 1, \cdots, J. j = 1, \cdots, J - 1 corresponds to different proposal mixture components, and j = J corresponds to the defensive mixture component.
`rpname`	name of user definded function rpname(n, mixture.param). It generates n random samples from target distribution pname. Parameters can be specified in mixture.param. rpname returns an n \times p matrix.