Selected publications
FarmTest: Factor Adjusted Robust Multiple Testing
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.
Main functions:
farm.test(X,...): onesample multiple tests;
farm.test(X,Y,...): twosample multiple tests.
Reference:
 Fan, J., Ke, Y., Sun, Q., and Zhou, W.X. (2017).
FARMTest: Factoradjusted robust multiple testing with false discovery control.
Manuscript.
 Zhou, W.X., Bose, K., Fan, J. and Liu, H. (2017).
A new perspective on robust Mestimation: Finite sample theory and applications to dependenceadjusted multiple testing.
Annals of Statistics, to appear
 Bose, K., Fan, J., Yuan, K. and Zhou, W.X. (2017).
FarmTest: An R Package for FactorAdjusted Robust Multiple Testing.
Manuscript
FarmSelect: Factor Adjusted Robust Model Selection
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.
Usage: farm.res(X, K.factors = NULL, robust = FALSE)
Reference:
 Fan, J., Ke, Y., Wang, K. (2017).
Decorrelation of Covariates for High Dimensional Sparse Regression
Manuscript.
Matlab codes for Adaptive Huber estimation
This is the matlab codes used for simulation and real data analysis for the paper below. It computes robust mean regression for highdimensional feature space with variable selection.
Reference:
 Fan, J., Li, Q., and Wang, Y. (2017).
Estimation of highdimensional mean regression in absence of symmetry and lighttail assumptions. Journal of Royal Statistical Society B , 79, 247265.

pfa: an R package for "Estimates False Discovery Proportion Under Arbitrary Covariance Dependence"
by Jianqing Fan, Tracy Ke, Sydney Li and Lucy Xia
This package contains functions for performing multiple testing and estimating the false discovery
proportion (FDP) under dependence.
Main functions: pfa.test(X,...): onesample multiple tests;
pfa.test(X,Y,...): twosample multiple tests.
pfa.gwas(X,Y,...): multiple testing in the genomewise association study (GWAS).
See Manual
Reference:
 (2011)
Nonparametric independence screening in sparse ultrahigh dimensional additive models.
Journal of American Statistical Association, 116, 544557.

POET: an R package for
estimating large covariance matrices by thresholding principal orthogonal complements.
by Fan, J., Liao, Y., and Mincheva, M. (2012)
Main function: POET performs PCA, estimate factor loadings, realized factors, and estimate sparse residual matrix by adaptive thresholding and the covariance matrix
See Manual
Reference:
Fan, J., Liao, Y. and Micheva, M. (2013).
Large Covariance Estimation by Thresholding Principal Orthogonal Complements.
(with discussion)
Journal of Royal Statistical Society B , to appear.

SIS: an R package for
(Iterative) Sure Independence Screening for generalized linear models and Cox's proportional
hazards models.
by Fan, J., Feng, Y., Samworth, R. J. and Wu, Y. (2010)
Main function: SIS performs variable selection using iteratively twoscale methods (largescale screenings followed by moderatescale selections). It calls automatically the functions GLMvanISISscad
and its variant GLMvarISISscad for Generalized linear models, and functions
COXvanISISscad and its variant COXvarISISscad for Cox's proportional models. Many other functions are available and can be called directly or by SIS using nondefault options. Examples are scadglm (a onestep method) and fullscadglm, and scadcox (a onestep method) and fullscadcox
See Manual
Related papers: procedures can be computed by the package
 Fan, J., Feng, Y. and Song, R. (2011)
Nonparametric independence screening in sparse ultrahigh dimensional additive models.
Journal of American Statistical Association, 116, 544557.
 Fan, J., Samworth, R. and Wu, Y. (2009)
Ultrahigh dimensional variable selection: beyond the lienar model.
Journal of Machine Learning Research, 10, 18291853.
 Fan, J. and Lv, J. (2008)
Sure independence screening for ultrahigh dimensional feature space.
(with discussion) Journal of Royal Statistical Society B, 70, 849911.
 Fan, J. and Li, R. (2001)
Variable selection via nonconcave penalized likelihood and its oracle properties.
Journal of American Statistical Association, 13481360.
 Fan, J. and Niu, Y. (2007)
Selection and validation of normalization methods for cDNA microarrays using withinarray replications.
Bioinformatics, 23, 23912398.
Rcode used for data analysis: Some adaptations are needed
Matlabcode used for simulation studies
 CCode for bandwidth selection of the conditional mean and variance functions
To compile, type "cc l autovar.c" and then "run a.out" and follow the instructions on the screen.
 Fan, J. and Yao, Q. (1998)
Efficient estimation of conditional variance functions in stochastic regression.
Biometrika, 85, 645660.
SplusCode for computing the Adaptive Neyman statistic: aneyman.s
SplusCode for computing the Hanova statistic: hanava.s
CCode for computing the Pvalue of the adaptive neyman statistic.
To compile, type: cc neyman.c lm. To run, type a.out and then following the instructions on the screen.
Fan, J. and Lin, S.K. (1998)
Test of Significance when data are curves.
Journal of American Statistical Association, 93, 10071021.