Title: Tool For Analysis of Images in Experiments
Description: Tools created for image analysis in researches. There are functions associated with image editing, segmentation, and obtaining biometric measurements (Este pacote foi idealizado para para a analise de imagens em pesquisas. Ha funcoes associadas a edicao de imagens, segmentacao, e obtencao de medidas biometricas).
Packaged: 2021-08-10 02:07:22 UTC; AlcineiAzevedo

Packaged: 2021-09-06 10:47:40 UTC; AlcineiAzevedo

Suggests: BiocManager, EBImage, knitr, rmarkdown
Packaged: 2021-12-11 01:07:37 UTC; AlcineiAzevedo

Packaged: 2022-01-23 01:10:22 UTC; AlcineiAzevedo

Suggests: BiocManager, rgdal, EBImage, knitr, rmarkdown

Author: Larisa Zamora-Matamoros and Jorge Diaz-Silvera
Description: Allows to calculate the probabilities  of occurrences of an event in a
great number of repetitions of Bernoulli experiment, through the application of
the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson.
Gives the possibility to show the results graphically and analytically, and to compare
the results obtained by the application of the above  theorems with those calculated by
the direct application of the Binomial formula. Is basically useful for educational

Title: Design of Discrete Choice and Conjoint Analysis
Description: Supports designing efficient discrete choice experiments (DCEs). Experimental designs can be formed on the basis of orthogonal arrays or search methods for optimal designs (Federov or mixed integer programs). Various methods for converting these experimental designs into a discrete choice experiment. Many efficiency measures! Draws from literature of Kuhfeld (2010) and Street et. al (2005) <doi:10.1016/j.ijresmar.2005.09.003>.
Imports: stats, far, dplyr, DoE.base, rlist, purrr
Packaged: 2020-04-03 14:11:56 UTC; Jed Stephens
Author: Jed Stephens [aut, cre] (<https://orcid.org/0000-0002-0131-703X>)

Title: Explanation of Predictions for Classification and Regression
Description: Package contains methods to generate explanations for individual predictions of
classification and regression models. Weighted averages of individual explanations form explanation of the whole model.
The package extends 'CORElearn' package, but other prediction models can also be explained using a wrapper.
Depends: CORElearn (>= 1.47.0),semiArtificial (>= 2.0.0)