c(person("Hyebin", "Song", role = c("aut", "cre"),
email = "hsong56@wisc.edu"),
person("Garvesh", "Raskutti", role="aut",email="raskutti@stat.wisc.edu"))

Efficient algorithm for solving PU (Positive and Unlabelled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2017) <arXiv:1711.08129>.
Rcpp (>= 0.12.8), methods, Matrix, doParallel, foreach,
ggplot2

Rcpp (>= 0.12.8), methods, Matrix, doParallel, foreach

Efficient algorithm for solving PU (Positive and Unlabeled) problem in low or high dimensional setting with lasso or group lasso penalty. The algorithm uses Maximization-Minorization and (block) coordinate descent. Sparse calculation and parallel computing are supported for the computational speed-up. See Hyebin Song, Garvesh Raskutti (2018) <arXiv:1711.08129>.

c(person("Hyebin", "Song", role = c("aut", "cre"),
email = "hps5320@psu.edu"),
person("Garvesh", "Raskutti", role="aut",email="raskutti@stat.wisc.edu"))

Population Viability Analysis with Data Cloning
Likelihood based population viability analysis in the
presence of observation error and missing data.  The package
can be used to fit, compare, predict, and forecast various
growth model types using data cloning.
R (>= 2.15.0), methods, dcmle, dclone, stats4

Likelihood based population viability analysis in the
presence of observation error and missing data.
The package can be used to fit, compare, predict,
and forecast various growth model types using data cloning.

Thiago Santos, Jose Alexandre Diniz-Filho, Thiago Rangel e Luis
Mauricio Bini
Computes Phylogenetic eigenVectors Regression and
Phylogentic Signal-Representation curve (with null and neutral
expectancies).

Computes phylogenetic eigenvectors regression (PVR) and
phylogenetic signal-representation curve (PSR) (with null and
Brownian expectations)