Authors@R: person("Joseph", "Stachelek", role = c("aut", "cre"),

Authors@R: person("Jemma", "Stachelek", role = c("aut", "cre"),
Suggests: gstat,gdata,spatstat,rgdal, testthat, knitr, rmarkdown
Author: Jemma Stachelek [aut, cre] (<https://orcid.org/0000-0002-5924-2464>)

Description: Access data from IPEA (Instituto de Pesquisa Economica Aplicada) hosted
at <http://www.ipeadata.gov.br>. It gets metadata and values for all series on
Imports: data.table, RCurl, dplyr, httr, jsonlite

person("Luiz Eduardo S.","Gomes", role=c("aut","cre"), email="gomes.leduardo@gmail.com"),
person("Jessyka A. P.","Goltara", role=c("ctb"), email="jessykagoltara@gmail.com")
Author: Luiz Eduardo S. Gomes [aut, cre],
Maintainer: Luiz Eduardo S. Gomes <gomes.leduardo@gmail.com>
Description: Allows directly access to the macroeconomic,
financial and regional database maintained by
Brazilian Institute for Applied Economic Research ('Ipea').
This R package uses the 'Ipeadata' API. For more information,
Imports: jsonlite, magrittr, dplyr, lubridate, purrr, sjlabelled, curl

Description: Allows direct access to the macroeconomic,
Imports: curl, stringr, jsonlite, magrittr, dplyr, lubridate, purrr,

Imports: stringr, curl, jsonlite, magrittr, dplyr, lubridate, purrr,

Imports: stringr, jsonlite, magrittr, dplyr, lubridate, purrr,

Imports: stringr, curl, rlang, jsonlite, magrittr, dplyr, lubridate,

Title: Integrative Lasso with Penalty Factors
Author: Anne-Laure Boulesteix, Mathias Fuchs
Description: The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen by cross-validation.