Description: Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available.

rasterImage, stats, graphics, grDevices, MASS

Imports: NMF, oro.nifti, tcltk, nnls, rasterImage, stats, graphics,

Description: Manipulate and visualize people data in meaningful and
common ways. This package is meant for Human Resources analytics
(often referred to as talent analytics or people analytics).

Description: Manipulate, analyze, and visualize people data in meaningful ways.
This package is meant for practitioners of Human Resources analytics (often
referred to as talent analytics or people analytics).

Description: Toolkit for leveraging people data.

Title: Toolkit for Data Analytics in Human Resources
Description: Transform employee data in meaningful ways. Two functions, 'hierarchyLong' and
'hierarchyWide', convert standard employee and supervisor relationship data
into useful formats for human resources (HR) analytics.

Description: Transform and analyze workforce data in meaningful ways for
human resources (HR) analytics. Two functions, 'hierarchyLong' and 'hierarchyWide',
convert standard employee and supervisor relationship data into useful formats.
A 'workforcePlan' app is available for simple workforce planning.
Imports: data.tree, data.table, shiny, rhandsontable, knitr, shinyjs,

Imports: data.tree, data.table, shiny, rhandsontable, knitr

Description: Transform and analyze workforce data in meaningful ways for human resources (HR) analytics.

Description: Transform and analyze workforce data in meaningful ways for human resources (HR) analytics. Get started with workforce planning using a simple Shiny app.

Imports: data.table, shiny, rhandsontable, knitr

Title: hierarchical Bayesian species distribution models
Author: Ghislain Vieilledent, Andrew M. Latimer, Alan E. Gelfand, Cory
Merow, Adam M. Wilson, Frederic Mortier and John A. Silander
Description: hSDM is an R package for estimating parameters of
hierarchical Bayesian species distribution models. Such models
allow interpreting the observations (occurrence and abundance
of a species) as a result of several hierarchical processes
including ecological processes (habitat suitability, spatial
dependence and anthropogenic disturbance) and observation
processes (species detectability). Hierarchical species
distribution models are essential for accurately characterizing
the environmental response of species, predicting their
probability of occurrence, and assessing uncertainty in the