Calculate Dissimilarity Matrix for Dataset with Mixed Attributes
Implement the methods proposed by Ahmad & Dey (2007) <doi:10.1016/j.datak.2007.03.016> in calculating the dissimilarity matrix at the presence of mixed attributes. This Package includes functions to discretize quantitative variables, calculate conditional probability for each pair of attribute values, distance between every pair of attribute values, significance of attributes, calculate dissimilarity between each pair of objects.

A Graphical User Interface for Plotting Common Univariate
Distributions
c(person("Christopher", "Casement", email = "casementc@gmail.com",
role = c("cre", "aut")),
person("Grant", "Innerst", role = "aut"),
person("Melissa", "Innerst", role = "aut")
)
Package including an interactive Shiny application for plotting
common univariate distributions.
Christopher Casement [cre, aut],
Grant Innerst [aut],
Melissa Innerst [aut]

A simple way to fit detection functions to distance sampling
data and calculate abundance/density for biological
populations.
A library that provides 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 if survey area information is provided.
'ds.R' 'create.bins.R' 'model.description.R' 'plot.dsmodel.R'
'print.dsmodel.R' 'print.summary.dsmodel.R' 'summary.dsmodel.R'

A library that provides 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 if survey area information is provided.

Distance Sampling Detection Function and Abundance Estimation
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.