Transition matrices (probabilities or counts) estimation for discrete Markov Chains of order n (1 <= n <= 5).

Multivariate Cumulative Sum (MCUSUM) Control Chart
mcusum package perform the Multivariate Cumulative Sum
(MCUSUM) Control Chart according to Crosier(1988)

The package performs Monte Carlo hypothesis tests. It allows a couple of different sequential stopping boundaries (a truncated sequential probability ratio test boundary and a boundary proposed by Besag and Clifford, 1991). Gives valid p-values and confidence intervals on p-values.

Monte Carlo hypothesis tests with Sequential Stopping
The package performs Monte Carlo hypothesis tests. It
allows a couple of different sequential stopping boundaries (a
truncated sequential probability ratio test boundary and a
boundary proposed by Besag and Clifford, 1991). Gives valid
p-values and confidence intervals on p-values.

Monte Carlo Hypothesis Tests with Sequential Stopping
Performs Monte Carlo hypothesis tests, allowing a couple of different sequential stopping boundaries. For example, a truncated sequential probability ratio test boundary (Fay, Kim and Hachey, 2007 <DOI:10.1198/106186007X257025>) and a boundary proposed by Besag and Clifford, 1991 <DOI:10.1093/biomet/78.2.301>. Gives valid p-values and confidence intervals on p-values.

Conjoint Analysis through Averaging of Multiple Analyses
The package aids in creating a Conjoint Analysis design
with extra cards.  Unlike traditional "holdout" cards these
cards are used to create a set of "good" (balanced and low
correlation) designs. Each of these designs is analyzed and the
average calculated.

Provides 3D plots of the Moran-Downton bivariate exponential distribution (MDBED),
generates bivariate random values, and also provides values of the joint and conditional PDFs and CDFs.
Nagao M, Kadoya M (1971) <http://hdl.handle.net/2433/124795>.
Balakrishna N, Lai CD (2009) <doi:10.1007/b101765>.

Calculates minimum detectable difference for several continuous and binary endpoints.  Also contains programs to compare the MDD to the clinically significant difference for most of the same tests.