exactcond: Exact Conditional Reference Distributions for Tables and
Selected Linear Models
Reference distributions that are exact at every sample size, obtained by conditioning
on a statistic sufficient for the nuisance parameter rather than by appealing to an asymptotic
approximation. Two forms of conditioning are supplied. For a two-way table the row and column totals are
sufficient for the marginal parameters, so conditioning on both margins leaves the Fisher-Yates
law, sampled here exactly by random permutation of the column labels; statistics include
Goodman's linear-by-linear association and, for nominal rows against an ordinal column, the
dispersion of the row mean scores, whose components are the group contrasts themselves. A
two-table form conditions further on the total score to cancel a common association parameter,
giving a nuisance-free test that two tables share it. For a linear model under a null in which
the tested structure is absent, a statistic invariant to the coefficients and the error scale
depends on the data only through the residual direction, which is uniform on the sphere of the
residual space; the reference stays exact when the statistic is maximised over candidate
structures, and so remains valid after a spatial weight matrix has been chosen by fit. Every
replicate draws from its own stream, so results do not depend on the thread count. The same C
sources back the 'Python' package 'exactcond'.
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=exactcond
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
[aut, cre]