The permutation test for matched pairs (PTMP) is a special case of MRBP, univariate data in two groups and n blocks, used for paired comparisons. Options allows PTMP to perform permutation versions of paired t-tests and Wilcoxon's signed rank test (for ranked data).
Least absolute deviation (LAD) regression is an alternative to ordinary least squares (OLS) regression that has greater power for thick-tailed symmetric and asymmetric error distributions (Cade and Richards 1996). LAD regression estimates the conditional median (a conditional 0.50 quantile) of a dependent variable given the independent variable(s) by minimizing sums of absolute deviations between observed and predicted values. LAD regression can be used anywhere OLS regression would be used but is often more desirable because it is less sensitive to outlying data points and is more efficient for skewed error distributions as well as some symmetric error distributions.
The PTMPObj class holds model specification and output from the Permutation test for matched pairs proceedure. The summary.PTMPObj class represents the summary of these objects.
G-sample and goodness-of-fit tests based on empirical coverages (COV) are for univariate comparisons of grouped data similar to the Kolmogorov-Smirnov family of statistics for comparing cumulative distribution functions (Mielke and Yao 1988, 1990). These statistics are appropriate for continuous univariate responses with no or few tied values. Options allow for testing goodness-of-fit to a uniform distribution on the unit circle, which is equivalent to a permutation version of Rao's spacing test (Rao 1976).
The MRBPObj class holds model specification and output from the Multiresponse Randomized Block procedure. The summary.MRBPObj class represents the summary of these objects.
Routines include a permutation version of ordinary least squares (OLS) regression that parallels the least absolute deviation (LAD) regression permutation test; a permutation and asymptotic chi-square approximation of P-values for a rank score statistic for regression quantiles; empirical coverage tests for univariate goodness-of-fit and g-sample comparisons that are extensions of the Kolmogorov-Smirnov family of statistics for comparing cumulative distribution functions; computing exact probabilities by complete enumeration of all possible combinations for small block and treatment designs in multiresponse randomized block permutation procedures (MRBP); a Monte Carlo resampling approximation alternative for all the MRPP family of statistics; and multivariate medians and distance quantiles (MEDQ) to be used as descriptive statistics with MRPP analyses; double permutation hypothesis tests procedures for linear model tests (OLS, LAD regression, and quantile rank score tests) when null models are either implicitly or explicitly constrained through the origin, i.e., no intercept models (Cade 2003, Cade et al. 2005, Cade et al. 2006); dropping all but a single zero residual in LAD (and quantile) regression permutation tests of subsets of variables in multiple regression models (Cade 2005, Cade and Richards 2006); and computation of all quantile regression estimates.
The LADObj class holds model specification and output from the Least Absolute Deviation lad and Hypothesis Test hypothesis.test commands. The summary.LADObj class represents the summary of these objects.
The MEDQObj class holds model specification and output from the Multivariate Medians and Distance Quantiles procedure. The summary.MEDQObj class represents the summary of these objects.
providing 
.