A general-purpose workflow for image segmentation using TensorFlow models based on the U-Net architecture by Ronneberger et al. (2015) <arXiv:1505.04597> and the U-Net++ architecture by Zhou et al. (2018) <arXiv:1807.10165>. We provide pre-trained models for assessing canopy density and understory vegetation density from vegetation photos. In addition, the package provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on grayscale or color images, both for binary and multi-class image segmentation.

Simple 'htmlwidgets' Image Viewer with WebGL Brightness/Contrast
Iakov Pustilnik [aut, cre],
Denis Rastegaev [aut]
c(
person("Iakov", "Pustilnik", email = "xyapus@gmail.com", role = c("aut", "cre")),
person("Denis", "Rastegaev", email = "leda82@gmail.com", role = "aut")
)
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.

Simulate General Insurance Policies and Losses
person("Brian", "Fannin", email = "FanninQED@Yahoo.com", role = c("aut", "cre"))
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.
dplyr, magrittr, assertthat, lubridate, stringi

person("Brian", "Fannin", email = "bfannin@casact.org", role = c("aut", "cre"))
Simulate general insurance policies, losses and loss emergence. The functions contemplate
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.
assertthat, checkmate, distributions3, dplyr, lubridate,
magrittr, rlang, stringi, tibble