V8.4.0 (R2020b release)

TRANSFORMATIONS IN REGRESSION

We have enriched the properties of the data transformations of the Yeo and Johnson (2000) for negative and positive responses, which we introduced in R2020a. More specifically, we intervened on the smoothness condition that the second derivative of zYJ(lambda) with respect to y be smooth at y = 0, along Atkinson et al (2019) and (2020), to allow two values of the transformations parameter: lambdaN for negative observations and lambdaP for non-negative ones. Now, function ScoreYJall computes:

1. a global t test associated with the constructed variable for lambda=lambdaP=lambdaN.

2. a t test for positive observations.

3. a t test for negative observations.

4. a F test for the joint presence of the two constructed variables described in points 2) and 3.

5. the F test based on the maximum liklihood estimate of lambdaP and lambdaN

New function ScoreYJall which computes the score tests described in points 1)-5) above.

New function ScoreYJmle which computes, in the case of extended Yeo and Johnson transformation, the likelihood ratio test of H0: lambdaP=lamabdaP0 and lambdaN=lambdaNeg0.

Added option usefmin in function boxcoxR. This option uses the solver (fminsearch or fminunc) to find MLE of the two transformation parameters for extended Yeo and Johnson family (Atkinson et al. 2020).

New function fanBIC which takes in input the output of FSRfan and using BIC and smoothness index enables to automatically choose in an efficient and robust way, the best value of the transformation parameter.

New function fanBICpn which enables to automatically choose the best values of the transformation parameters for positive and negative observations

New function normYJpn which extends the companion functions normBoxCox and normYJ to the case of extended Yeo and Johnson transformation.

TIME SERIES

New function SETARX which implements Threshold autoregressive models with two regimes

ROBUST CLUSTERING

New tools for dealing with the 14 Gaussian parsimonious clustering models (GPCM).

In function genSigmaGPCM new option pa.exactrestriction has been added. If pa.exactrestriction is true the covariance matrices are generated with the exact values of the restrictions specified in pa.cdet, pa.shw and pa.swb. In function MixSim optional input structure sph now can be called with field sph.exactrestriction

In function tclust the fourth input restrfactor can be a structure which can contain the type of Gaussian Parsimonious Clustering Model - GPCM (restrfactor.pars), the
scalars in the interval [1 Inf) which specifies the
the restriction which have to be applied to the determinants (restrfactor.cdet), to the elements of the shape matrices inside each group (restrfactor.shw) and across groups (restrfactor.shb).

New functions tclustICgpcm, tclustICsolGPCM, tclustICplotGPCM, and carbikeplotGPCM which extend functions tclustIC, tclustICsol, tclustICplot and carbikeplot to the case of the 14 GPCM.

GRAPHICS

New plots waterfallchart (which implements the waterfall chart (see https://en.wikipedia.org/wiki/Waterfall_chart) and new function funnelchart which implements the funnel chart (see
https://en.wikipedia.org/wiki/Funnel_chart)

new function scatterboxplot (which creates scatter diagram with marginal boxplots).

Improvment to functions

Now spmplot accepts as input a table. In the case the names of the tables are automatically added at the margins. Similarly, in function corrNominal when option datamatrix is true it is possible to supply as first argument a table.

DATASETS

New datasets balancesheets and facemasks added in the datasets regression section and datasets clustering section respectively