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‎_development/FSreg.m

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%
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‎addFSDA2path.m

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%
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% See also path
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‎clustering/ClusterRelabel.m

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% https://doi.org/10.1080/10618600.2017.1390469
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% Copyright 2008-2021.
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‎clustering/ComputePars.m

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% To be precise, sums in (1) and (3) are for l:li(i,j,l) is different from 1
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% Similarly, sums in (2) and (4) are for l:li(i,j,l) is = 1
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%
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‎clustering/FSMmmdrs.m

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% "Mining Massive Data Sets for Security", F. Fogelman-Soulie et al. Eds.,
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% pp. 271-286, IOS Press.
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%
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% Copyright 2008-2021.
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‎clustering/FSRmdrrs.m

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% "Mining Massive Data Sets for Security", F. Fogelman-Soulie et al. Eds.,
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% pp. 271-286, IOS Press.
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%
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% Copyright 2008-2021.
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‎clustering/GetOmegaMap.m

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% Davies, R. (1980), The distribution of a linear combination of
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% chi-square random variables, Applied Statistics, 29, pp. 323-333.
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%
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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‎clustering/GetOmegaMapv1.m

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% highest overlap (largest off diagonal element of matrix
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% OmegaMap)
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%
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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% Written by FSDA team
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‎clustering/GowerIndex.m

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% This function has been written jointly with Professor Aurea Grane',
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% Universidad Carlos III de Madrid, Statistics Department.
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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‎clustering/MixSim.m

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% Eigenvalues and Eigenvectors, in Bauer, F.L. Eds, "Handbook for Automatic
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% Computation", Vol. 2, pp. 315-326, Springer.
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%
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% Copyright 2008-2021.
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‎clustering/MixSimreg.m

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% Analysis and Classification". [doi
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% https://doi.org/10.1007/s11634-018-0331-4].
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‎clustering/cpcE.m

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% ..., k
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% Omega2D : p-by-p matrix containing the updated common rotation matrix.
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%
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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% Written by FSDA team
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‎clustering/cpcV.m

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% components in high dimensions, Advances in Data Analysis and
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% Classification, Vol. 8, pp. 217-226
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% Copyright 2008-2021.
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% Written by FSDA team
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%$LastChangedDate:: $: Date of the last commit
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‎clustering/ctlcurves.m

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% "Exploring the number of groups in robust model-based
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% clustering." Statistics and Computing, Vol. 21, pp. 585-599.
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%
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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‎clustering/dempk.m

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% Pairwise Overlap, "Journal of Computational and Graphical Statistics",
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% Vol. 25, pp. 66-90.
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%
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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% Written by FSDA team
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%
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% <a href="matlab: docsearchFS('dempk')">Link to the help page for this function</a>

‎clustering/estepFS.m

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%
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% McLachlan, G.J.; Peel, D. (2000). Finite Mixture Models. Wiley. ISBN 0-471-00626-2
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%
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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%
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%<a href="matlab: docsearchFS('estepFS')">Link to the help function</a>
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%

‎clustering/genSigmaGPCM.m

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% Celeux, G., Govaert, G. (1995), Gaussian parsimonious clustering models,
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% "Pattern Recognition", 28, pp. 781-793.
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%
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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% Written by FSDA team
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‎clustering/initR.m

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% Omega : p-by-p-k 3D array containing in position j Omega2D.
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% This is the common rotation matrix replicated k times.
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% Written by FSDA team
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%$LastChangedDate:: $: Date of the last commit
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‎clustering/lga.m

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% Grouping Using Orthogonal Regression, "Computational Statistics and Data
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% Analysis", Vol. 50, pp. 1287-1312.
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%
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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% Written by FSDA team
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%
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‎clustering/linear_grouping_plot.m

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% hp = hyperplane
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% out = output structure returned by function lga or rlga
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% Written by FSDA team
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‎clustering/overlap.m

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% Davies, R. (1980), The distribution of a linear combination of
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% chi-square random variables, "Applied Statistics", Vol. 29, pp. 323-333.
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%
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% Copyright 2008-2021.
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% Written by FSDA team
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‎clustering/overlapmap.m

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%
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% ...
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%
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% Copyright 2008-2021.
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% Written by FSDA team
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%
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%<a href="matlab: docsearchFS('overlapmap')">Link to the help page for this function</a>

‎clustering/restrSigmaGPCM.m

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% Robust parsimonious clustering models. Submitted.
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%
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%
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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% Written by FSDA team
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‎clustering/restrdeter.m

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% [Available at
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% http://www.eio.uva.es/infor/personas/tclust_algorithm.pdf]
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%
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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% Written by FSDA team
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%
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% DETAILS. This algorithm solves the minimization problem with constraints

‎clustering/restrdeterGPCM.m

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% Robust parsimonious clustering models. Submitted.
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%
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% Written by FSDA team
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‎clustering/restreigen.m

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% [Available at
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% http://www.eio.uva.es/infor/personas/tclust_algorithm.pdf]
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%
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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% Written by FSDA team
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%
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% DETAILS. This algorithm solves the minimization problem with constraints

‎clustering/restreigeneasy.m

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% [Available at
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% http://www.eio.uva.es/infor/personas/tclust_algorithm.pdf]
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%
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% Copyright 2008-2021.
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% Written by FSDA team
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%
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% DETAILS. This algorithm solves the minimization problem with constraints

‎clustering/restreigenmemopt.m

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% [Available at
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% http://www.eio.uva.es/infor/personas/tclust_algorithm.pdf]
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%
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% Written by FSDA team
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% DETAILS. This algorithm solves the minimization problem with constraints

‎clustering/restrshapeExact.m

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%
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% References:
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‎clustering/restrshapeGPCM.m

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% Robust parsimonious clustering models. Submitted.
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%
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% Copyright 2008-2021.
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% Written by FSDA team
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‎clustering/rlga.m

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% [doi: 10.1111/j.1467-9868.2008.00682.x]
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%
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‎clustering/simdataset.m

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% "Advances in data analysis and classification", Vol. 9, pp. 461-481.
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% [DOI 10.1007/s11634-015-0223-9].
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%
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‎clustering/simdatasetreg.m

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% Davies, R. (1980), The distribution of a linear combination of
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% chi-square random variables, "Applied Statistics", Vol. 29, pp. 323-333.
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%
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% Written by FSDA team
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%<a href="matlab: docsearchFS('simdatasetreg')">Link to the help function</a>

‎clustering/tclust.m

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% www.eio.uva.es/inves/grupos/representaciones/trTCLUST.pdf]
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‎clustering/tclustIC.m

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% Likelihoods, "Journal of Computational and Graphical Statistics", pp. 404-416,
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% https://doi.org/10.1080/10618600.2017.1390469
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%
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‎clustering/tclustICgpcm.m

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% Likelihoods, "Journal of Computational and Graphical Statistics", pp. 404-416,
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% https://doi.org/10.1080/10618600.2017.1390469
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%
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‎clustering/tclustICsol.m

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% Classification", Vol. 2, pp. 193-218.
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%
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% Written by FSDA team
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%<a href="matlab: docsearchFS('tclustICsol')">Link to the help function</a>

‎clustering/tclustICsolGPCM.m

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% Classification", Vol. 2, pp. 193-218.
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%
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%
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% Written by FSDA team
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%<a href="matlab: docsearchFS('tclustICsolGPCM')">Link to the help function</a>

‎clustering/tclustcore.m

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function out = tclustcore(Y,Cini,Sigmaini,Niini,reftol,refsteps,mixt,equalweights,h,nselected,k,restrnum,restrfactor,userepmat,nParam)
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% This function is called by tclusteda and it is not intended to be called directly
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% Written by FSDA team
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%$LastChangedDate:: $: Date of the last commit
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‎clustering/tclusteda.m

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% www.eio.uva.es/inves/grupos/representaciones/trTCLUST.pdf]
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%
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‎clustering/tclustreg.m

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% Methodologies for Clustering Linear Regression Data, "Advances in Data
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% Analysis and Classification".
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%
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% Copyright 2008-2021.
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% Copyright 2008-2023.
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% Written by FSDA team
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%
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%<a href="matlab: docsearchFS('tclustreg')">Link to the help page for this function</a>

‎clustering/tclustregIC.m

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% Analysis and Classification". [doi
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% https://doi.org/10.1007/s11634-018-0331-4].
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%
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‎clustering/tclustregcore.m

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% This function is called by tclustregeda and it is not intended to be called directly
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‎clustering/tclustregeda.m

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% Analysis and Classification".
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%
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