Title: Simple 'htmlwidgets' Image Viewer with WebGL Brightness/Contrast
person("Iakov", "Pustilnik", email = "xyapus@gmail.com", role = c("aut", "cre")),
person("Denis", "Rastegaev", email = "leda82@gmail.com", role = "aut")
Description: Display a 2D-matrix data as a interactive zoomable gray-scale image viewer, providing tools for manual data inspection. The viewer window shows cursor guiding lines and a corresponding data slices for both axes at the current cursor position. A tool-bar allows adjusting image display brightness/contrast through WebGL filters and performing basic high-pass/low-pass filtering.

Title: Simulate General Insurance Policies and Losses
Authors@R: person("Brian", "Fannin", email = "FanninQED@Yahoo.com", role = c("aut", "cre"))
Description: Simulate general insurance policies, losses and loss emergence. The package contemplates
deterministic and stochastic policy retention and growth scenarios. Retention and growth rates are percentages relative
to the expiring portfolio. Claims are simulated for each policy. This is accomplished either be assuming a frequency
distribution per development lag or by generating random wait times until claim emergence and settlement. Loss simulation
uses standard loss distributions for claim amounts.
Imports: dplyr, magrittr, assertthat, lubridate, stringi

Authors@R: person("Brian", "Fannin", email = "bfannin@casact.org", role = c("aut", "cre"))
Description: Simulate general insurance policies, losses and loss emergence. The functions contemplate
Imports: assertthat, checkmate, distributions3, dplyr, lubridate,

Title: Imaging Engine, Tools for Application of Image Filters to Data
Description: Provides fast application of image filters to data matrices,

Title: IMAGing engINE, Tools for Application of Image Filters to Data

Packaged: 2018-05-06 15:21:24 UTC; NvTomus InDemonic

Title: IMAGing engINEs, Tools for Application of Image Filters to Data

Title: Preprocessing Algorithms for Imbalanced Datasets
person("Ignacio", "Cordón", email = "nacho.cordon.castillo@gmail.com", role = c("aut", "cre"))
Description: Algorithms to treat imbalanced datasets. Imbalanced datasets usually damage
the performance of the classifiers. Thus, it is important to treat data before
applying a classifier algorithm. This package includes recent preprocessing algorithms
Imports: bnlearn, KernelKnn, ggplot2, utils, stats, mvtnorm, Rcpp
Suggests: testthat, C50, knitr, rmarkdown, FNN, smotefamily