Algorithm for Ranking Predictors Using Graphical Domain
Knowledge
Grows families of features by selecting features that maximize a weighted score calculated from empirical feature scores and graphical knowledge. The final weighted score for a feature is determined by summing a feature's family-weighted scores across all families in which the feature appears.

c(person("Koushiki", "Bose", email = "bose@princeton.edu", role = c("aut", "cre")), person("Yuan", "Ke", email = "keyuan_princeton@163.com", role = "aut"),person("Kaizheng", "Wang", email = "kzwang2015@gmail.com", role = "aut"))
Implements a consistent model selection strategy for high dimensional sparse regression when the covariate dependence can be reduced through factor models. By separating the latent factors from idiosyncratic components, the problem is transformed from model selection with highly correlated covariates to that with weakly correlated variables. It is appropriate for cases where we have many variables compared to the number of samples. Moreover, it implements a robust procedure to estimate distribution parameters wherever possible, hence being suitable for cases when the underlying distribution deviates from Gaussianity. See the paper on the 'FarmSelect' method, Fan et al.(2017) <arXiv:1612.08490>, for detailed description of methods and further references.
graphics, stats, grDevices, utils, methods, Rcpp, glmnet,
ncvreg
Koushiki Bose [aut, cre],
Yuan Ke [aut],
Kaizheng Wang [aut]

graphics, stats, grDevices, utils, methods, Rcpp, ncvreg,
fBasics

c(person("Koushiki", "Bose", email = "bose@princeton.edu", role = c("aut", "cre")), person("Yuan", "Ke", email = "keyuan_princeton@163.com", role = "aut"),person("Wen-xin", "Zhou", email = "wez243@ucsd.edu", role = "aut"))
Performs robust multiple testing for means in the presence of known and unknown latent factors. It implements a robust procedure to estimate distribution parameters using the Huber's loss function and accounts for strong dependence among coordinates via an approximate factor model. This method is particularly suitable for high dimensional data when there are many variables but only a small number of observations available. Moreover, the method is tailored to cases when the underlying distribution deviates from Gaussian, which is commonly assumed in the literature. Besides the results of hypotheses testing, the estimated underlying factors and diagnostic plots are also output. Multiple comparison correction is done after estimating the proportion of true null hypotheses using the method in Storey (2015) <https://github.com/jdstorey/qvalue>. See the paper on the 'FarmTest' method, Zhou et al.(2017) <https://goo.gl/68SJpd>, for detailed description of methods and further references.
graphics, stats, Rcpp, grDevices, utils, methods
Koushiki Bose [aut, cre],
Yuan Ke [aut],
Wen-xin Zhou [aut]