Opens a 'shiny' app which supports theoretical and computational analysis of block designs for symmetrical and mixed level factorial experiments. This package includes tools to check whether a design has orthogonal factorial structure (OFS) with balance or not and is able to find the orthogonality deviation value if not having OFS. This package includes function  to evaluate efficiency factor of all factorial effects in two situations, in the first situation if the design is verified with OFS and balance then calculate the efficiencies of all factorial effects using a specific analytical procedure  and in the second situation if the design is verified with non-OFS and balance then a new general method has been developed and used to calculate efficiencies under the condition that the design should be proper and equi-replicated, See Gupta, S.C. and Mukerjee, R. (1987): "A Calculus for factorial arrangements".  Lecture Notes in Statistics. No. 59, Springer-Verlag, Berlin, New York, <doi:10.1007/978-1-4419-8730-3>. For the easy use of package, 'shiny' app is used for giving inputs and inputs validation.

Implements instrumental variable estimators for 2^K factorial experiments with noncompliance.

Perform Maximum Likelihood Factor analysis on a covariance matrix or data matrix.

Some functions of 'ade4' and 'stats' are combined in order to obtain 
             a partition of the rows of a data table, with columns representing 
             variables of scales: quantitative, qualitative or frequency. 
             First, a principal axes method is performed and then, a combination 
             of Ward agglomerative hierarchical classification and K-means is 
             performed, using some of the first coordinates obtained from the 
             previous principal axes method. 
             In order to permit different weights of the elements to be clustered, 
             the function 'kmeansW', programmed in C++, is included. 
             It is a modification of 'kmeans'. Some graphical functions include 
             the option: 'gg=FALSE'.  When 'gg=TRUE', they  use the 'ggplot2' 
             and 'ggrepel' packages to avoid  the super-position of the labels.