c(person(given = "Bradley",
family = "Rava",
role = c("aut", "cre"),
email = "brava@marshall.usc.edu"),
person(given = "Wenguang",
family = "Sun",
role = "ctb"),
person(given = "Xin",
family = "Tong",
role = "ctb"),
person(given = "Gareth",
family = "James",
role = "ctb"))
The Fair Adjusted Selective Inference (FASI) procedure as described in the paper "A Fairness-Adjusted Selective Inference Framework For Classification" By: Bradley Rava, Wen Sun, Gareth James and Xin Tong (2021). The package includes a function that preforms the FASI procedure and returns both the r-scores and classification for new observations.
R (>= 3.5.0), naivebayes, fastAdaboost, gam, stats
Bradley Rava [aut, cre],
Wenguang Sun [ctb],
Xin Tong [ctb],
Gareth James [ctb]

A Fast and Scalable Joint Estimator for Learning Multiple
Related Sparse Gaussian Graphical Models
The FASJEM (A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models) is a joint estimator which is fast and scalable for learning multiple related sparse Gaussian graphical models. For more details, please see <https://www.cs.virginia.edu/yanjun/paperA14/2017_JEM_combined.pdf>.

This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool  can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.

Analyze, Summarize, and Visualize Daily Streamflow Data
c(person("Jon", "Goetz", email = "jon.goetz@gov.bc.ca", role = c("aut", "cre")),
person("Carl James", "Schwarz", email = "cschwarz.stat.sfu.ca@gmail.com", role = "aut"),
person("Robin", "Pike", email = "robin.g.pike@gov.bc.ca", role = "ctb"),
person("Province of British Columbia", role = "cph"))
The Flow Analysis Summary Statistics Tool for R, 'fasstr', provides various
functions to clean and screen daily stream discharge data; calculate and visualize various summary statistics
and metrics; and compute annual trending (using 'zyp' package methods <https://CRAN.R-project.org/package=zyp>)
and volume frequency analyses (using methods similar to HEC-SSP (2019)
<https://www.hec.usace.army.mil/software/hec-ssp/>). It features useful function arguments for filtering of and
handling dates, customizing data and metrics, and the ability to pull daily data directly from the Water Survey
of Canada hydrometric database (<https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>).
Jon Goetz [aut, cre],
Carl James Schwarz [aut],
Robin Pike [ctb],
Province of British Columbia [cph]