Description: Select cost-efficient factors using Branching LARS method

Authors@R: c(person("Donal", "Phipps", email = "donal.phipps@gmail.com", role = c("aut", "cre")))
Description: Interact with the 'Brandwatch' API <https://developers.brandwatch.com/docs>.
Allows you to authenticate to the API and obtain data for projects, queries, query groups tags and categories.
Also allows you to directly obtain mentions and aggregate data for a specified query or query group.
Imports: httr (>= 1.3.1), jsonlite (>= 1.5), data.table (>= 1.10)

Title: Test for Parallel Regression Assumption
Author: Benjamin Schlegel [aut,cre], Marco Steenbergen [aut]
Description: Tests the parallel regression assumption for ordinal logit models generated with the function polr() from the package MASS.

Description: Tests the parallel regression assumption for ordinal logit models generated with the function polr() from the package 'MASS'.

Packaged: 2018-01-10 08:38:11 UTC; Benjamin Schlegel

Description: Tests the parallel regression assumption wit the brant test by Brant (1990) <doi: 10.2307/2532457> for ordinal logit models generated with the function polr() from the package 'MASS'.
Packaged: 2020-09-22 06:18:07 UTC; Benjamin Schlegel

Description: Executes BASIC programs from the 1970s, for historical and
educational purposes. This enables famous examples of early machine
learning, artificial intelligence, natural language processing, cellular
automata, and so on, to be run in their original form.

Title: Bayesian Screening and Variable Selection
Author: Dongjin Li [aut, cre], Somak Dutta [aut], Vivekananda Roy [ctb]
Description: Performs Bayesian variable selection with embedded screening for ultra-high dimensional Gaussian linear regression models. The methodology is described in Li, Dutta and Roy (2020) <arXiv:2006.07561>.
Imports: Rcpp (>= 1.0.2), Matrix (>= 1.2-17), compiler
Packaged: 2020-09-04 03:18:30 UTC; liyangxiaobei

Description: Performs Bayesian variable screening and selection for ultra-high dimensional linear regression models.