qrcmNP - Nonlinear and Penalized Quantile Regression Coefficients Modeling
Penalized and nonlinear parametric modeling of quantile regression coefficient functions. The penalized integrated loss minimization estimator (lasso and group lasso penalties) of Sottile, Frumento, Chiodi and Bottai (2020) <doi:10.1177/1471082X19825523> performs covariate selection using the information on all quantiles simultaneously. Nonlinear parametric quantile models, in which the conditional quantile function can be a nonlinear function of the parameters, of the covariates or both, are estimated by integrated loss minimization as in Bottai and Cilluffo (2020) <doi:10.1177/0962280220941159>. Both approaches extend the quantile regression coefficients modeling framework of Frumento and Bottai (2016) <doi:10.1111/biom.12410> implemented in 'qrcm'.
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group-lassolassononlinear-modelsquantile-regressionstatisticsvariable-selection
3.81 score 13 scripts 281 downloadsQest - Quantile-Based Estimators
Quantile-based estimators (Q-estimators) fit any parametric distribution through its quantile function, by minimizing the integrated loss of quantile regression. They are usually more robust than maximum likelihood estimators, and allow for censored and truncated data. The package implements user-defined quantile functions, built-in families (normal, gamma, continuous Poisson, uniform), linear models and proportional hazards models. The method is described in Sottile and Frumento (2022) <doi:10.1016/j.csda.2022.107471>.
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censored-datam-estimationproportional-hazardsquantile-functionquantile-regressionrobust-statisticsstatisticssurvival-analysistruncated-data
3.70 score 250 downloadsclustEff - Clusters of Effects Curves in Quantile Regression Models
Clustering of curves based on a dissimilarity that combines the concordance of their shapes and their closeness, as proposed by Sottile and Adelfio (2019) <doi:10.1007/s00180-018-0817-8>. The method can be applied to the effects curves of quantile regression coefficients models fitted with 'qrcm', with univariate or multivariate responses, and to curves observed in time, as in functional data analysis. The package also implements the clustering of waveforms by trimmed k-means of functional principal component scores (Adelfio, Chiodi, D'Alessandro and Luzio, 2011).
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clusteringcurve-clusteringfunctional-data-analysishierarchical-clusteringquantile-regressionstatisticswaveform-clustering
3.70 score 8 scripts 346 downloads
islasso - The Induced Smoothed Lasso
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; see <doi:10.1177/0962280219842890> and discussed in a tutorial <doi:10.13140/RG.2.2.16360.11521>.
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openblasfortranglibc
3.70 score 8 scripts 497 downloadschangepointsVar - Change-Points Detections for Changes in Variance
Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, <doi:10.1080/03610918.2011.592248>.
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1.00 score 4 scripts 355 downloads