Multidimensional Iterative Proportional Fitting and Alternative Models
Transforming an array to a vector
Expand a Table in a Data Frame
Extracts the deviation between every target and generated margin
Update an N-way table given target margins
Extract the coefficients of the estimates from an object of class mipf...
Comparing deviations of mipfp objects
Computes the marginal matrix A and margins vector m of an estimation p...
Computing confidence intervals for the mipfp estimates
Converting correlation to odds ratio
Converting correlation to pairwise probability
Flatten a table, array or matrix
Computing confidence intervals for the estimated counts and probabilit...
Extracting the linearly independant columns from a matrix
Wald, Log-likelihood ratio and Person Chi-square statistics for mipfp ...
Multidimensional Iterative Proportional Fitting
Covariance matrix of the estimators produced by Ipfp (deprecated)
Multidimensional Iterative Proportional Fitting and Alternative Models
Estimating a contingency table using model-based approaches
Generating a multivariate Bernoulli joint-distribution
Converting odds ratio to correlation
Converting odds ratio to pairwise probability
Simulating a multivariate Bernoulli distribution
Summarizing objects of class mipfp
Calculate variance-covariance matrix for mipfp objects
Transforming a vector to an array
An implementation of the iterative proportional fitting (IPFP), maximum likelihood, minimum chi-square and weighted least squares procedures for updating a N-dimensional array with respect to given target marginal distributions (which, in turn can be multidimensional). The package also provides an application of the IPFP to simulate multivariate Bernoulli distributions.