Title: Estimating extended Rasch models.
Author: Patrick Mair, Reinhold Hatzinger
Description: eRm estimates Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).
Packaged: Sat Oct 14 11:34:34 2006; admin

Description: eRm fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).
Packaged: Wed Oct 18 14:18:48 2006; admin

Description: eRm allows the estimation of Rasch models and extendsions such as: Linear logistic test model (LLTM), rating scale model (RSM), linear rating scale model (LRSM), partial credit model (PCM), and linear partial credit model (LPCM)
Packaged: Tue Sep 12 21:31:32 2006; admin

Description: eRm fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM). Missing values are allowed in the data matrix. Additional features are the estimation of the person parameters, LR-Model test, item-specific Wald test, itemfit and personfit statistics, various ICC plots. An eRm platform is provided at http://r-forge.r-project.org/projects/erm/.
Packaged: Tue Aug 28 12:36:11 2007; admin

Packaged: Thu Apr 26 11:24:56 2007; admin

Description: eRm fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM). Missing values are allowed in the data matrix. Additional features are the estimation of the person parameters, LR-Model test, item-spefific Wald test, itemfit and personfit statistics, various ICC plots.
Packaged: Wed May 23 09:39:46 2007; admin

Packaged: Fri May 18 11:02:07 2007; admin

Title: Ensemble Tool for Predictions from Species Distribution Models
Description: A tool which allows users to create and evaluate ensembles
of species distribution model (SDM) predictions.
Functionality is offered through R functions or a GUI (R Shiny app).
This tool can assist users in identifying spatial uncertainties and
making informed conservation and management decisions. The package is
further described in Woodman et al (in review - contact author for paper).
Authors@R: person("Sam", "Woodman", email = "sam.woodman@noaa.gov", role = c("aut", "cre"))
Imports: colorRamps, colourpicker, dichromat, dplyr (>= 0.7-0), DT,
leaflet, lwgeom, methods, purrr, raster, rlang, RColorBrewer,
ROCR, shiny, shinycssloaders, shinydashboard, shinyjs, sf (>=
0.6-3), stats, tmap (>= 2.1), units, viridis
Suggests: knitr, maps, maptools, mapview, rmarkdown, testthat (>=
Packaged: 2019-07-23 18:02:49 UTC; sam.woodman