This method computes three statistics to perform a test wheter the seed agrees with the target data. The statistics are the Wilk's log-likelihood ratio statistic, the Wald statistic and the Person Chi-square statistic.
Corr2Odds
(Package: mipfp) :
Converting correlation to odds ratio
For K binary (Bernoulli) random variables X_1, ..., X_K, this function transforms the correlation measure of association C_ij between every pair (X_i, X_j) to the odds ratio O_ij where
This method returns the maximum deviation between each generated and desired margins of the input argument. It corresponds to the absolute maximum deviation between each target margin used to generate the estimates in the mipfp object and the generated one.
RMultBinary
(Package: mipfp) :
Simulating a multivariate Bernoulli distribution
This function generates a sample from a multinomial distribution of K dependent binary (Bernoulli) variables (X_1, X_2, ..., X_K) defined by an array (of 2^K cells) detailing the joint-probabilities.
Transform a N-dimensional array a to vector. The transformation is done assuming that the last index of the array moves fastest. For instance, an array a of dimensions (2,2,2) will produce the vector v = ( a[1,1,1], a[1,1,2], a[1,1,3], a[1,2,1], a[1,2,2],…,a[3,3,3] ).
This function computes the (asymptotic) Wald confidence intervals at a given significance level for the results generated by Ipfp and ObtainModelEstimates (provided that their option compute.cov was set to TRUE).
This function takes a multidimensional object and flattens it for a pretty printing. The row names are the concatenation of the original dimension names while the only column stores the initial data of the object.
This function provides several alternative estimating methods to the IPFP when estimating a multiway table subject to known constrains/totals: maximum likelihood method (ML), minimum chi-squared (CHI2) and weighted least squares (WLSQ). Note that the resulting estimators are probabilities.
providing 
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