
robustbase
 Referenced in 448 articles
[sw07114]
 allowing to analyze data with robust methods. This includes regression methodology including model selections ... where we strive to cover the book ”Robust Statistics, Theory and Methods” by Maronna, Martin...

robCompositions
 Referenced in 13 articles
[sw11804]
 analysis for compositional data (Fisher rule), robust regression with compositional predictors and (robust) AndersonDarling...

KNITRO
 Referenced in 190 articles
[sw00490]
 nonconvex. It is also effective for nonlinear regression, problems with complementarity constraints (MPCCs or MPECs ... MINLP). KNITRO is highly regarded for its robustness and efficiency. KNITRO provides a wide range...

PROGRESS
 Referenced in 11 articles
[sw26316]
 least median of squares (LMS) regression method is highly robust to outliers in the data ... means of PROGRESS (from Program for RObust reGRESSion). After ten years we have developed...

chemCal
 Referenced in 13 articles
[sw11069]
 optionally weighted  linear regression (lm) or robust linear regression (rlm from the MASS package...

DepthProc
 Referenced in 9 articles
[sw17738]
 scatter estimators, multivariate Wilcoxon tests and robust regressions...

FSDA
 Referenced in 18 articles
[sw11737]
 include the main traditional robust multivariate and regression techniques, including ... typical model assumptions, tools are available for robust data transformation and robust model selection. When...

TOMCAT
 Referenced in 3 articles
[sw01049]
 Continuum Power Regression, Partial Robust MRegression, Robust Continuum Regression and Radial Basis Functions Partial...

linLIR
 Referenced in 4 articles
[sw11393]
 basis of likelihood inference. These regression functions are said to be undominated. Since ... interval data can be unbounded, a robust regression method is necessary. Hence, we consider ... robust LIR method based on the minimization of the residuals’ quantiles. For this method ... interceptslope pairs corresponding to the undominated regression functions is the union of finitely many...

BLINWDR
 Referenced in 3 articles
[sw27839]
 BLINWDR: Robust and bounded influence regression. BLINWDR is a single FORTRAN program which represents ... extension of the widely used linear robust curve fitting program LINWDR. Besides options for different ... handle the problem of distributional robustness in regression, the program offers the choice between several...

DPpackage
 Referenced in 67 articles
[sw10495]
 order to gain modeling flexibility and robustness against misspecification of the probability model ... probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function...

EDAseq
 Referenced in 5 articles
[sw10345]
 level effects) on read counts: loess robust local regression, globalscaling, and fullquantile normalization...

LIBRA
 Referenced in 28 articles
[sw10553]
 contains userfriendly implementations of several robust procedures. These methods are resistant to outliers ... skewness, multivariate location and covariance estimation (MCD), regression (LTS, MCDregression), Principal Component Analysis (RAPCA ... Principal Component Regression (RPCR), Partial Least Squares Regression (RSIMPLS), classification (RDA, RSIMCA), clustering, outlier detection ... depth quantiles. For comparison also several nonrobust functions are included. Many graphical tools...

robustHD
 Referenced in 6 articles
[sw14244]
 robustHD: Robust Methods for HighDimensional Data. Robust methods for highdimensional data, in particular ... model selection techniques based on least angle regression and sparse regression...

hdm
 Referenced in 4 articles
[sw21313]
 models. Including functions for fitting heteroscedastic robust Lasso regressions with nonGaussian errors...

LibSkylark
 Referenced in 2 articles
[sw22709]
 toolkit for leastsquares and robust regression, eigenvector analysis, nonnegative matrix factorization, and other...

wwcode
 Referenced in 19 articles
[sw26044]
 estimates can be calculated using an L1 regression routine. More importantly, we present a collection ... that can be used to implement a robust analysis of a linear model based ... weighting schemes are discussed. We analyze a regression model, designed experiment, and autoregressive time series...

OPELM
 Referenced in 22 articles
[sw12171]
 with additional steps to make it more robust and generic. The whole methodology is presented ... detail and then applied to several regression and classification problems. Results for both computational time...

xtscc
 Referenced in 7 articles
[sw37339]
 Robust standard errors for panel regressions with crosssectional dependence. I present a new Stata ... leastsquares regression and fixedeffects (within) regression models with Driscoll and Kraay (Review ... type test for fixed effects that is robust to general forms of crosssectional...

nlr
 Referenced in 1 article
[sw31352]
 package nlr: Nonlinear Regression Modelling using Robust Methods. NonLinear Robust package is developed ... problem of outliers in nonlinear regression, using robust statistics. It covers classic methods in nonlinear ... include tools to detecting outliers in nonlinear regression. (Riazoshams H, Midi H, and Ghilagaber ... ISBN:9781118738061). Robust Nonlinear Regression, with Application using R, John Wiley...