Wong (1982) <DOI:10.2307/2287296>), and multiplicative factor
Four different link functions accommodate different
discussed in Hoff, Fosdick, Volfovsky and Stovel (2013)

Several different link functions accommodate different
relational data structures, including binary/network data, normal
relational data, zero-inflated positive outcomes using a tobit model, ordinal relational data and data from
fixed-rank nomination schemes. Several of these link functions are

Description: Fitting generalized additive mixed models based on the
Collate: 'amer-class.R' 'amer.R' 'amerSetup.R' 'expand.call.R'
'expandBasis.R' 'expandMf.R' 'getF.R' 'indsF.R' 'plotF.R'
'set.mfrow.R' 'subAZ.R' 'subFcts.R' 'tp.R' 'tpU.R'

Depends: methods, nlme, Matrix, splines, lme4
Suggests: mlmRev, SASmixed, mgcv, ggplot2
'reparameterizeDesign.R' 'tp.R' 'tpU.R' 'bsp.R' 'tpU2d.R'

Title: Interface to 'AmeriFlux' Data Services
Description: Programmatic interface to the 'AmeriFlux' database
Imports: heatmaply, httr, jsonlite, memoise, RCurl, readxl, tools
Suggests: covr, data.table, knitr, pander, qpdf, remotes, rmarkdown,
Author: Housen Chu [aut, cre] (<https://orcid.org/0000-0002-8131-4938>),

Title: American Politics-Inspired Color Palette Generator
Authors@R: person("Philip", "Waggoner", role = c("aut", "cre"),
email = "philip.waggoner@gmail.com", comment = c(ORCID = "0000-0002-7825-7573"))
Description: A color palette generator inspired by American politics, with colors ranging from blue on the
left to gray in the middle and red on the right. A variety of palettes allow for a range of applications
from brief discrete scales (e.g., three colors for Democrats, Independents, and Republicans) to
continuous interpolated arrays including dozens of shades graded from blue (left) to red (right). This
package greatly benefitted from building on the source code (with permission) from Ram and Wickham (2015).
Author: Philip Waggoner [aut, cre] (<https://orcid.org/0000-0002-7825-7573>)

Authors@R: c(person("Dong", "Wang", role = c("aut", "cre"), email = "dwangstat@gmail.com"))
Description: This package implements the adaptive mixed lasso (AML) method proposed by Wang et al.(2011).  AML applies adaptive lasso penalty to a large number of predictors,  thus producing a sparse model, while accounting for the population structure in the linear mixed model framework.  The package here is primarily designed for application to genome wide association studies or genomic prediction in plant breeding populations, though it could be applied to other settings of linear mixed models.