The method=0 uses MATLAB function randperm. In old MATLAB releases
randperm was slower than FSDA function shuffling, which is used in
method 1 (for example, in R2009a - MATLAB 7.8 - randperm was at least
50 slower).

If method=1 the approach depends on the population and sample sizes:

- if $n < 1000$ and $k < n/(10 + 0.007n)$, that is if the population is
relatively small and the desired sample is small compared to the
population, we repeatedly sample with replacement until there are k
unique values;

- otherwise, we do a random permutation of the population and return
the first k elements.

The threshold $k < n/(10 + 0.007n)$ has been determined by simulation
under MATLAB R2016b. Before, the threshold was $n < 4*k$.

If method=2 systematic sampling is used, where the starting point is
random and the step is also random.

If method=3 random sampling is based on the old but well known Linear
Congruential Generator (LCG) method. In this case there is no guarantee
to get unique numbers. The method is included for historical reasons.

If method is a vector of n weights, then Weighted Sampling Without
Replacement is applied. Our implementation follows Efraimidis and
Spirakis (2006). MATLAB function datasample follows Wong and Easton
(1980), which is also quite fast; note however that function datasample
may be very slow if applied repetedly, for the large amount of time
spent on options checking.

Remark on computation performances. Method=2 (systematic sampling) is
by far the fastest for any practical population size $n$. For example,
for $n \approx 10^6$ method=2 is two orders of magniture faster than
method=1. With recent MATLAB releases (after R2011b) method = 0 (which
uses compiled MATLAB function randperm) has comparable performances, at
least for reasonably small $k$. In releases before 2012a, randperm was
considerably slow.