Ddf
(Package: MissMech) :
Hessian of the observed datat Multivariate Normal Log-Likelihood with Incomplete Data
The Hessian of the normal-theory observed data log-likelihood function, evaluated at a given value of the mean vector and the covariance matrix, when data are incomplete. The output is a symmetric matrix with rows/columns corresponding to elements in the mean vector and lower diagonal of the covariance matrix.
This function rearranges the data based on their missing data patterns. Morever, missing data patterns consisting of fewer than the user specified number in del.lesscases is deleted from the dataset.
This is a non-parametric K-sample test that tests equality of distribution of a variable between k populations based on samples from each of the populations.
TestMCARNormality
(Package: MissMech) :
Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random
The main purpose of this package is to test whether the missing data mechanism, for an incompletely observed data set, is one of missing completely at random (MCAR). As a by product, however, this package has the capabilities of imputing incomplete data, performing a test to determine whether data have a multivariate normal distribution, performing a test of equality of covariances for groups, and obtaining normal-theory maximum likelihood estimates for mean and covariance when data are incomplete. The test of MCAR follows the methodology proposed by Jamshidian and Jalal (2010). It is based on testing equality of covariances between groups having identical missing data patterns. The data are imputed, using two options of normality and distribution free, and the test of equality of covariances between groups with identical missing data patterns is performed also with options of assuming normality (Hawkins test) or non-parametrically. Users can optionally use their own method of data imputation as well. Multiple imputation is an additional feature of the program that can be used as a diagnostic tool to help identify cases or variables that contribute to rejection of MCAR, when the MCAR test is rejecetd (See Jamshidian and Jalal, 2010 for details). As explained in Jamshidian, Jalal, and Jansen (2014), this package can also be used for imputing missing data, test of multivariate normality, and test of equality of covariances between several groups when data are completly observed.
TestUNey
(Package: MissMech) :
Test of Goodness of Fit (Uniformity)
This routine tests whether the values in a vector x is distributed as uniform (0,1). The Neyman's smooth test of fit, as described by Ladwina (1994) is used. The p-values are obtained based on a resampling method from uniform (0,1).
Mls
(Package: MissMech) :
ML Estimates of Mean and Covariance Based on Incomplete Data
Normal theory maximum likelihood estimates of mean and covariance matrix is obtained when data are incomplete, using EM algorithm (see Jamshidian and Bentler 1999). If the option Hessian is set to TRUE, then the observed information containing the standard errors of the parameter estimates is also computed.
Hawkins
(Package: MissMech) :
Test Statistic for the Hawkins Homoscedasticity Test
Produces the F_ij's and A_ij's that are used in the Hawkins' test of homogeneoity of covariances. See Hawkins (1981) and Jamshidian and Jalal (2010) for more details.
providing 
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