Robust methods for high-dimensional data
Rcpp (>= 0.9.10), cvTools (>= 0.3.0), lattice, MASS,
robustbase (>= 0.6-0)
Robust methods for high-dimensional data, in particular
linear model selection techniques based on least angle
regression and sparse regression.
'AIC.R' 'backend.R' 'coef.R' 'corHuber.R' 'cv.R' 'fastLasso.R'
'fitModels.R' 'fitted.R' 'initialSubsets.R' 'lambda0.R'
'plot.R' 'predict.R' 'print.R' 'repCV.R' 'residuals.R'
'rlars.R' 'sparseLTS.R' 'standardize.R' 'test.R' 'utils.R'
'weights.R' 'winsorize.R' 'zzz.R'

'accessors.R' 'AIC.R' 'backend.R' 'coef.R' 'corHuber.R'
'fastLasso.R' 'fitted.R' 'fortify.R' 'initialSubsets.R'
'lambda0.R' 'perry.R' 'plot.R' 'predict.R' 'print.R'
'residuals.R' 'rlars.R' 'seqModel.R' 'sparseLTS.R'
'standardize.R' 'subset.R' 'test.R' 'utils.R' 'weights.R'
'winsorize.R' 'zzz.R'

Rcpp (>= 0.9.10), RcppArmadillo (>= 0.3.0), MASS, parallel,
pcaPP, robustbase (>= 0.6-0), stats
Robust methods for high-dimensional data, in particular linear
model selection techniques based on least angle regression and sparse
regression.

Robust Methods for High-Dimensional Data

MASS, Rcpp (>= 0.9.10), grDevices, parallel, stats, utils

Perform the Robust Multivariable Mendelian Randomization
Analysis
Perform the robust multivariable Mendelian randomization 'robustMVMR'
analysis in the two-sample Mendelian randomization setting. The 'robustMVMR' package
produces both the robust estimators and the robust standard errors via the MM-estimates,
which has been demonstrated to protect against heteroskedasticity, autocorrelation,
and the presence of outliers in Yohai (1987) <doi:10.1214/aos/1176350366> and
Croux (2003) <https://EconPapers.repec.org/RePEc:ete:ceswps:ces0316>.

Perform the robust multivariable Mendelian randomization 'robustMVMR'
analysis in the two-sample Mendelian randomization setting. An example paper
using the 'robustMVMR' package can be found in Yang et al. (2021)
<doi:10.1080/15412555.2021.1955848>. In details, the 'robustMVMR' package
produces both the robust estimators and the robust standard errors
via the MM-estimates, which has been demonstrated to protect against
heteroskedasticity, autocorrelation, and the presence of outliers in
Yohai (1987) <doi:10.1214/aos/1176350366> and
Croux (2003) <https://EconPapers.repec.org/RePEc:ete:ceswps:ces0316>.