Imports: rcdd, mapproj, geometry, pbmcapply, utils, plot3D, imager,
Packaged: 2018-09-19 13:54:19 UTC; rafaelmaia

Imports: geometry, pbmcapply, plot3D, readbitmap, rcdd, sp, viridisLite
Suggests: digest, imager, knitr, mapproj, rgl, rmarkdown, spelling,
Packaged: 2019-03-06 00:06:50 UTC; rafaelmaia
Author: Rafael Maia [aut, cre] (<https://orcid.org/0000-0002-7563-9795>),

Description: A cohesive framework for parsing, analyzing and organizing colour
Imports: geometry, pbmcapply, plot3D, rcdd, sp, magick, cluster,
Suggests: digest, imager, knitr, mapproj, rgl, rmarkdown, testthat,
Packaged: 2019-06-25 03:31:24 UTC; rafaelmaia

Description: A cohesive framework for parsing, analyzing and
Imports: cluster, geometry(>= 0.4.0), magick, pbmcapply, plot3D, sp,
Suggests: digest, imager, knitr, mapproj, NbClust, rgl, rmarkdown,
Packaged: 2019-12-11 07:12:43 UTC; rafaelmaia

Imports: cluster, future.apply, geometry(>= 0.4.0), lightr(>= 1.0),
magick, plot3D, progressr, sp, viridisLite
Packaged: 2020-02-08 06:39:24 UTC; rafaelmaia

Imports: alphashape3d, cluster, future.apply, geometry(>= 0.4.0),
lightr(>= 1.0), magick, plot3D, progressr, sp, viridisLite
Author: Thomas White [aut, cre] (<https://orcid.org/0000-0002-3976-1734>),

Suggests: alphashape3d, digest, imager, knitr, mapproj, NbClust, rgl,

magick, farver, plot3D, progressr, sp, viridisLite
Suggests: alphashape3d, digest, imager, knitr, mapproj, rgl, rmarkdown,

Description: A cohesive framework for the spectral and spatial analysis of
colour described in Maia, Eliason, Bitton, Doucet & Shawkey (2013)

Packaged: 2022-08-16 06:12:21 UTC; thomaswhite

Title: Physical activity with accelerometers
Description: Process, format and store accelerometer data
Packaged: 2013-11-13 19:43:28 UTC; Marco Geraci

Title: Physical Activity with Accelerometers
Imports: stats, utils, graphics, SparseM
Description: This is a collection of functions to process, format and store accelerometer data.

Packaged: 2014-06-20 13:21:49 UTC; Marco Geraci

Title: Penalized Adaptive Weighted Least Squares Regression
Description: Efficient algorithms for fitting weighted least squares regression with \eqn{L_{1}}{L1} regularization on both the
coefficients and weight vectors, which is able to perform simultaneous variable selection
Packaged: 2017-05-11 02:05:39 UTC; Administrator