A simple way of fitting detection functions to distance sampling
data for both line and point transects. Adjustment term selection, left and
right truncation as well as monotonicity constraints and binning are
supported. Abundance and density estimates can also be calculated (via a
Horvitz-Thompson-like estimator) if survey area information is provided. See
Miller et al. (2019) <doi:10.18637/jss.v089.i01> for more information on
methods and <https://examples.distancesampling.org/> for example analyses.

covr, progress, parallel, doParallel, foreach, testthat

covr, progress, parallel, doParallel, doRNG, foreach,
activity, testthat

covr, progress, parallel, doParallel, doRNG, foreach,
activity, testthat, optimx, readxl

DiSTATIS Three Way Metric Multidimensional Scaling
Derek Beaton [aut, com, ctb], Cherise Chin Fatt [ctb], Herve Abdi [aut, cre]
Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). For the  analysis of  multiple distance/covariance matrices collected on the same set of observations.

Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). For the  analysis of  multiple distance/covariance matrices collected on the same set of observations

c(
person("Alec James", "van Rassel", email = "alec.van-rassel.1@ulaval.ca",
role = c("aut", "cre", "cph")),
person("Gabriel", "Crépeault-Cauchon", role = c("aut", "ccp")),
person("Étienne", "Marceau", role = c("tch", "sad")),
person("Hélène", "Cossette", role = c("tch", "sad")),
person("Laboratoire Act & Risk", role = c("fnd", "sht")),
person("École d'actuariat de l'Université Laval", role = c("fnd", "his", "uvp")),
person("Natural Sciences and Engineering Research Council of Canada", role = c("fnd")),
person("Marc-André", "Devost", role = c("ccp"))
)
Calculates expected values, variance, different moments (kth
moment, truncated mean), stop-loss, mean excess loss, Value-at-Risk (VaR)
and Tail Value-at-Risk (TVaR) as well as some density and cumulative
(survival) functions of continuous, discrete and compound distributions.
This package also includes a visual 'Shiny' component to enable students
to visualize distributions and understand the impact of their parameters.
This package is intended to expand the 'stats' and 'actuar' packages so as
to enable students to develop an intuition for probability.
actuar, stats, shiny, dplyr, ggplot2, plotly, tippy,
shinyWidgets, shinydashboardPlus, shinydashboard, rlang
Alec James van Rassel [aut, cre, cph],
Gabriel Crépeault-Cauchon [aut, ccp],
Étienne Marceau [tch, sad],
Hélène Cossette [tch, sad],
Laboratoire Act & Risk [fnd, sht],
École d'actuariat de l'Université Laval [fnd, his, uvp],
Natural Sciences and Engineering Research Council of Canada [fnd],
Marc-André Devost [ccp]
Alec James van Rassel <alec.van-rassel.1@ulaval.ca>