**Edit:** Re: whether or not there are `R` programs to do this - if it's normally distributed data with a pre-specified _sample_ mean/covariance you're seeking to simulate, the `mvrnorm` function in `R` includes this option by setting `empirical=TRUE`. Here is a simple univariate example: 

    library(MASS)
    x = mvrnorm(n = 10000, rep(0,1), 1, tol = 1e-6, empirical = TRUE)
    mean(x)
    [1] -5.793152e-18
    var(x)
         [,1]
    [1,]    1

**Original answer:** In general, to make your sample mean and variance _exactly_ equal to a pre-specified value, you can appropriately shift and scale the variable. Specifically, if $X_1, X_2, ..., X_n$ is a sample, then the new variables 

$$ Z_i = \sqrt{c_{1}} \left( \frac{X_i-\overline{X}}{s_{X}} \right) + c_{2} $$ 

where $\overline{X} = \frac{1}{n} \sum_{i=1}^{n} X_i$ is the sample mean and $ s^{2}_{X} =  \frac{1}{n-1} \sum_{i=1}^{n} (X_i - \overline{X})^2$ is the sample variance are such that the sample mean of the $Z_{i}$'s is exactly $c_2$ and their sample variance is exactly $c_1$. A similarly constructed example can restrict the range - shift/scale the data (subtract the $\min$, divide by the $\max - \min$) so that the range is $(0,1)$, then appropriately re-shift/scale to get into the range $(a,b)$.