Linear regression functions using Huber and Bisquare psi
functions

Linear regression functions using Huber and Bisquare psi functions

Malay Ghosh, Jiyoun Myung, Fernando Moura
Functions for Robust Bayesian Small Area Estimation.
R(>= 3.1.0), MCMCpack, coda, lattice, mvtnorm, pscl

Robust Estimation for Seemingly Unrelated Regression Models
c(person(given = "Claudio",
family = "Agostinelli",
role = c("aut", "cre"),
email = "claudio.agostinelli@unitn.it",
comment = c(ORCID = "0000-0001-6702-4312")),
person(given = "Giovanni",
family = "Saraceno",
role = "aut",
email = "giovanni.saraceno@unitn.it",
comment = c(ORCID = "0000-0002-1753-2367"))
)
Data sets are often corrupted by outliers. When data are multivariate outliers can be classified as case-wise or cell-wise. The latters are particularly challenge to handle. We implement a robust estimation procedure for Seemingly Unrelated Regression Models which is able to cope well with both type of outliers. Giovanni Saraceno, Fatemah Alqallaf, Claudio Agostinelli (2021) <arXiv:2107.00975>.
Claudio Agostinelli [aut, cre]
(<https://orcid.org/0000-0001-6702-4312>),
Giovanni Saraceno [aut] (<https://orcid.org/0000-0002-1753-2367>)

Robust Estimation of Variance Component Models.
Claudio Agostinelli <claudio@unive.it> and Victor J. Yohai <victoryohai@gmail.com>
Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.

Robust Estimation of Variance Component Models
Claudio Agostinelli <claudio.agostinelli@unitn.it> and Victor J. Yohai <victoryohai@gmail.com>

Visualize the Results of Risk-of-Bias (ROB) Assessments
c(
person("Luke", "McGuinness",
role = c("aut", "cre"),
email = "luke.mcguinness@bristol.ac.uk"),
person("Emily", "Kothe", role = "ctb"))
Helps users in quickly visualizing risk-of-bias
assessments performed as part of a systematic review. It allows users to
create weighted bar-plots of the distribution of risk-of-bias judgments
within each bias domain, in addition to traffic-light plots of the
specific domain-level judgments for each study. The resulting figures are
of publication quality and are formatted according the risk-of-bias
assessment tool use to perform the assessments. Currently, the supported
tools are ROB2.0 (for randomized controlled trials; Sterne et al (2019)
<doi:10.1136/bmj.l4898>), ROBINS-I (for non-randomised studies of
interventions; Sterne et al (2016) <doi:10.1136/bmj.i4919>), and QUADAS-2
(for diagnostic accuracy studies; Whiting et al (2011)
<doi:10.7326/0003-4819-155-8-201110180-00009>).
Luke McGuinness [aut, cre],
Emily Kothe [ctb]