'Rcpp' Bindings to Native C++ Implementation of MS Numpress
c(person("Justin", "Sing", email = "justincsing@gmail.com", role = c("cre","aut")),
person("Johan", "Teleman", email = "johan.teleman@immun.lth.se", role = "aut"))
'Rcpp' bindings to the native C++ implementation of MS Numpress, that provides two compression schemes for numeric data from mass spectrometers. The library provides implementations of 3 different algorithms, 1 designed to compress first order smooth data like retention time or M/Z arrays, and 2 for compressing non smooth data with lower requirements on precision like ion count arrays. Refer to the publication (Teleman et al., (2014) <doi:10.1074/mcp.O114.037879>) for more details.
Justin Sing [cre, aut],
Johan Teleman [aut]

c(person("Xiucai", "Ding", role = c("aut", "cre","cph"),
email = "xiucaiding89@gmail.com"),
person("Yichen", "Hu", role = c("aut","cph"),
email = "ethhu@ucdavis.edu"))
We generate random variables following general Marchenko-Pastur distribution and Tracy-Widom distribution. We compute limits and distributions of eigenvalues and generalized components of spiked covariance matrices. We give estimation of all population eigenvalues of spiked covariance matrix model. We give tests of population covariance matrix. We also perform matrix denoising for signal-plus-noise model.
'Limits.R' 'CovEst.R' 'CovTest.R' 'GeneralMP.R'
'GeneralWishartMax.R' 'SignalPlusNoise.R'
MASS, RMTstat, lpSolve, mpoly, nleqslv, pracma, rARPACK,
rootSolve, quadprog
Xiucai Ding [aut, cre, cph],
Yichen Hu [aut, cph]

c(person("Han", "Cao", email=c("hank9cao@gmail.com", "han.cao@zi-mannheim.de"), role=c("cre", "aut", "cph")),
person("Emanuel", "Schwarz", email="emanuel.schwarz@zi-mannheim.de", role="aut"))
Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.
Han Cao [cre, aut, cph],
Emanuel Schwarz [aut]