GWLP(design, ...)
"GWLP"(design, kmax=design.info(design)$nfactors, attrib.out=FALSE, with.blocks = FALSE, digits = NULL, ...)
"GWLP"(design, kmax=ncol(design), attrib.out=FALSE, digits = NULL, ...)
Choose(n, k)
Kraw(k,x,n,q)
ham(c1, c2)
levels.no(xx)
levelmix(xx)
distDistmix(code, levm)
Bprime(dists, nmax=5)
dualDistmix(Bprime, nmax=5)design;
class design properties are exploited by using only factor columns
(or factor and block columns, if with.blocks is TRUE) TRUE, the block column contributes to
the GWLP, otherwise it does not NULL prevents rounding design design levelmix distDistmix,
analogous to the B_j1_j2 of p.1072 of Xu and Wu 2001kmax in calls by other functionsBprime, the MacWilliams transform
of the distance distributionGWLP is intended for direct use.The GWLP methods output a named vector with the numbers of generalized
words of lengths zero to kmax. If attrib.out is TRUE,
this vector comes with the attributes B and levels.info,
the latter documenting the level situation of the design, the former
the distance distribution B (Xu and Wu 2001).
GWLP is intended for direct use, the others are not.
Function GWLP is much faster but also more inaccurate than the
function lengths, which calculates numbers of words
for lengths 2 to 5 only. Note, however, that function lengths
can be faster for designs with very many rows.
Function ham calculates the Hamming distance, function Kraw
the Krawtchouk polynomials, function Choose differs from the base
function choose by treatment of negative values n,
functions levels.no and levelmix are utilities providing the
level information on the design xx.
The functions distDistmix, Bprime and dualDistmix
implement formulae from Xu and Wu (2001) for the distance distribution,
its MacWilliams transform and the calculation of GWLP from the latter.
lengths GWLP(L18)
GWLP(L18, attrib.out=TRUE)
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