• robustbase

  • Referenced in 429 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 12 articles [sw11804]
  • analysis for compositional data (Fisher rule), robust regression with compositional predictors and (robust) Anderson-Darling...
  • KNITRO

  • Referenced in 180 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 10 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 12 articles [sw11069]
  • optionally weighted - linear regression (lm) or robust linear regression (rlm from the MASS package...
  • 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...
  • DepthProc

  • Referenced in 8 articles [sw17738]
  • scatter estimators, multivariate Wilcoxon tests and robust regressions...
  • TOMCAT

  • Referenced in 3 articles [sw01049]
  • Continuum Power Regression, Partial Robust M-Regression, 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 ... intercept-slope 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 66 articles [sw10495]
  • order to gain modeling flexibility and robustness against mis-specification 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, global-scaling, and full-quantile normalization...
  • LIBRA

  • Referenced in 28 articles [sw10553]
  • contains user-friendly implementations of several robust procedures. These methods are resistant to outliers ... skewness, multivariate location and covariance estimation (MCD), regression (LTS, MCD-regression), 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 non-robust functions are included. Many graphical tools...
  • hdm

  • Referenced in 4 articles [sw21313]
  • models. Including functions for fitting heteroscedastic robust Lasso regressions with non-Gaussian errors...
  • LibSkylark

  • Referenced in 2 articles [sw22709]
  • toolkit for least-squares and robust regression, eigenvector analysis, non-negative matrix factorization, and other...
  • robustHD

  • Referenced in 5 articles [sw14244]
  • robustHD: Robust Methods for High-Dimensional Data. Robust methods for high-dimensional data, in particular ... model selection techniques based on least angle regression and sparse regression...
  • OP-ELM

  • 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...
  • wwcode

  • Referenced in 18 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...
  • xtscc

  • Referenced in 7 articles [sw37339]
  • Robust standard errors for panel regressions with cross-sectional dependence. I present a new Stata ... least-squares regression and fixed-effects (within) regression models with Driscoll and Kraay (Review ... type test for fixed effects that is robust to general forms of cross-sectional...
  • nlr

  • Referenced in 1 article [sw31352]
  • package nlr: Nonlinear Regression Modelling using Robust Methods. Non-Linear 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:978-1-118-73806-1). Robust Nonlinear Regression, with Application using R, John Wiley...