Title: Post-calibration of allele-specific copy-number estimates
R.utils (>= 1.7.8), matrixStats (>= 0.2.2), aroma.core (>=
Description: Post-calibration of allele-specific copy-number estimates

R.utils (>= 1.8.5), matrixStats (>= 0.3.0), aroma.core (>=
Imports: R.utils (>= 1.8.5), R.filesets (>= 1.1.0)

R.utils (>= 1.9.11), matrixStats (>= 0.4.4), aroma.core (>=
Imports: utils, R.utils (>= 1.9.11), R.filesets (>= 1.1.4)

Title: R Interface to Access CalPASS API
Description: Implements methods for querying data from CalPASS using its API.
CalPASS Plus.  MMAP API V1. <https://mmap.calpassplus.org/docs/index.html>.
Imports: httr, dplyr, digest, jsonlite, stringr

Description: Record and generate a 'gif' of your 'R' sessions plots. When creating
a visualization, there is inevitably iteration and refinement that occurs.
Automatically save the plots made to a specified directory, previewing them
as they would be saved. Then combine all plots generated into a 'gif'
Imports: ggplot2, gifski, tools, magick, rsvg, jsonlite, rlang,
Suggests: knitr, rmarkdown, markdown, ragg, grid, covr, testthat (>=
3.0.0), patchwork, withr, dplyr, systemfonts, scales, extrafont
Author: Ellis Hughes [aut, cre] (<https://orcid.org/0000-0003-0637-4436>),

Author: Xingguo Li, Tuo Zhao and Han Liu
Depends: R (>= 2.15.0), lattice, igraph, MASS, Matrix
Description: The package "camel" provides the implementation of a family of high-dimensional calibrated machine learning toolkits, including a family of Lasso regression and sparse Gaussian graphical model estimation. Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso for estimating high dimensional sparse linear model. The sparse Gaussian graphical model estimation includes TIGER and CLIME using L1 penalty. We adopt the combination of the dual smoothing and monotone fast iterative soft-thresholding algorithm (MFISTA). The computation is memory-optimized using the sparse matrix output.

Author: Xingguo Li, Tuo Zhao, and Han Liu
Description: The package "camel" provides the implementation of a family of high-dimensional calibrated machine learning tools, including (1) LAD, SQRT Lasso and Calibrated Dantzig Selector for estimating sparse linear models; (2) Calibrated Multivariate Regression for estimating sparse multivariate linear models; (3) Tiger, Calibrated Clime for estimating sparse Gaussian graphical models. We adopt the combination of the dual smoothing and monotone fast iterative soft-thresholding algorithm (MFISTA). The computation is memory-optimized using the sparse matrix output, and accelerated by the path following and active set tricks.