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Gianluca Sottile
Postdoc in Statistics at UNIPA
islasso
An implementation of the induced smoothing (IS) idea to lasso regularization models to allow estimation and inference on the model coefficients (currently hypothesis testing only). Linear, logistic, Poisson and gamma regressions with several link functions are implemented. The algorithm is described in the original paper: Cilluffo, G., Sottile, G., La Grutta, S. and Muggeo, V. (2019) The Induced Smoothed lasso: A practical framework for hypothesis testing in high dimensional regression. <doi:10.1177/0962280219842890>, and discussed in a tutorial: Sottile, G., Cilluffo, G., and Muggeo, V. (2019) The R package islasso: estimation and hypothesis testing in lasso regression. <doi:10.13140/RG.2.2.16360.11521>.
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go to the package page;
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go to the package manual;
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go to the technical report on ResearchGate.
qrcmNP
This package implements a nonlinear Frumento and Bottai's (2016) method for quantile regression coefficient modeling (qrcm), in which quantile regression coefficients are described by (flexible) parametric functions of the order of the quantile. In the classical qrcm framework the linearity in β(p) and/or in θ could be relaxed at a cost of more complicated expressions for the ojective and the gradient functions. Here, we propose an efficiently algorithm to use more flexible structures for the regression coefficients. With respect to the most famous function nlrq (quantreg package) our main function niqr implements the integrated quantile regression idea of Frumento and Bottai's (2016) for nonlinear functions. As already known, this practice allows to estimate quantiles all at one time and not one at a time. This package also implements a penalized Frumento and Bottai's (2015) method for quantile regression coefficient modeling (qrcm). This package fits lasso qrcm using pathwise coordinate descent algorithm. With respect to some other packages which implements the L1-quantile regression (e.g. quantreg, rqPen) estimating quantiles one at a time our proposal allows to estimate the conditional quantile function parametrically estimating quantiles all at one and to do variable selction in the meanwhile.
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go to the package page;
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go to the package manual.
clustEff
clustEff
This package implements a general algorithm to cluster coefficient functions (i.e. clusters of effects) obtained from a quantile regression (qrcm; Frumento and Bottai, 2016). This algorithm is also used for clustering curves observed in time, as in functional data analysis. The objectives of this algorithm vary with the scenario in which it is used, i.e. in the case of a cluster of effects, in a univariate case the objective may be to reduce its dimensionality or in the multivariate case to group similar effects on a covariate. In the case of a functional data analysis the main objective is to cluster waves or any other function of time or space.
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read the paper;
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go to the package page;
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go to the package manual.
changepointsVar
This algorithm allows breakpoint detections for changes in variation assuming that the variance function can be described by a piecewise constant function with segments delimited by unknown change-points. The approach is a generalization of the cumSeg procedure proposed by Muggeo and Adelfio (2011) assuming that testing for stepwise changes in variance of a sequence of Gaussian random variables may be transformed equivalently to the case of testing for changes in mean of the squared residuals (from an estimated linear model that accounts for the mean behavior of the observed signal) assuming a gamma GLM with a log-link function. A variation of lars procedure adapted to the GLM case is considered to discard the spurious change-points on the basis of a generalized version of the BIC. The proposed approach results in a very efficient algorithm even with n large and many change-points to be estimated.
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read the paper;
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go to the package page;
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go to the package manual.
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