reshape2, microbenchmark, emmeans (>= 1.4), estimability,
heavy, lqmm, rlme, robustvarComp, MASS, lemon, RColorBrewer,
skewt, fs, dplyr, ggh4x

Implements the Robust Scoring Equations estimator to fit linear
mixed effects models robustly.
Robustness is achieved by modification of the scoring equations
combined with the Design Adaptive Scale approach.
reshape2, microbenchmark, emmeans (>= 1.4), estimability,
lqmm, rlme, MASS, lemon, RColorBrewer, skewt, fs, dplyr, ggh4x

Robust estimation of the generalized log gamma model.
Claudio Agostinelli <claudio@unive.it>, Alfio Marazzi
<Alfio.Marazzi@chuv.ch>, V.J. Victor
<vyohai@uolsinectis.com.ar> and Alex Randriamiharisoa
<Alex.Randriamiharisoa@chuv.ch>
Robust estimation of the generalized log gamma model is
provided using Quantile Tau estimator and Weighted Likelihood
estimator.

Claudio Agostinelli <claudio@unive.it>, Alfio Marazzi
<Alfio.Marazzi@chuv.ch>, Victor Yohai
<vyohai@uolsinectis.com.ar> and Alex Randriamiharisoa
<Alex.Randriamiharisoa@chuv.ch>

Claudio Agostinelli <claudio@unive.it>, Alfio Marazzi <Alfio.Marazzi@chuv.ch>, Victor Yohai <vyohai@uolsinectis.com.ar> and Alex Randriamiharisoa <Alex.Randriamiharisoa@chuv.ch>
Robust estimation of the generalized log gamma model is provided using Quantile Tau estimator and Weighted Likelihood estimator.

Robust Estimation of the Generalized log Gamma Model
Claudio Agostinelli <claudio.agostinelli@unitn.it>, Alfio Marazzi <Alfio.Marazzi@chuv.ch>, Victor Yohai <vyohai@uolsinectis.com.ar> and Alex Randriamiharisoa <Alex.Randriamiharisoa@chuv.ch>
Robust estimation of the generalized log gamma model is provided using Quantile Tau estimator, Weighted Likelihood estimator and Truncated Maximum Likelihood estimator. Functions for regression and censored data are also available.
robustbase, survival, numDeriv, RobustAFT (>= 1.4-1)

Robust Inference for Meta-Analysis with Influential Outlying
Studies
c(person("Hisashi", "Noma", email="noma@ism.ac.jp", role=c("aut","cre")), person("Shonosuke", "Sugasawa", role=c("aut")), person("Toshi A.", "Furukawa", role=c("aut")))
Robust inference methods for fixed-effect and random-effects models of meta-analysis are implementable. The robust methods are developed using the density power divergence that is a robust estimating criterion developed in machine learning theory, and can effectively circumvent biases and misleading results caused by influential outliers. The density power divergence is originally introduced by Basu et al. (1998) <doi:10.1093/biomet/85.3.549>, and the meta-analysis methods are developed by Noma et al. (2022) <forthcoming>.
Hisashi Noma [aut, cre],
Shonosuke Sugasawa [aut],
Toshi A. Furukawa [aut]