Title: Density Surface Modelling of Distance Sampling Data
Description: Density surface modelling of line transect data. A Generalized
Additive Model-based approach is used to calculate spatially-explicit estimates
of animal abundance from distance sampling (also presence/absence and strip
transect) data. Several utility functions are provided for model checking,

Suggests: Distance, sp, tweedie, testthat

package DISTANCE earlier than version 6.2. For a version that works with

Description: Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.

Sharon Hedley, Megan Ferguson, Natalie Kelly.

Title: Data Science Box of Pandora Miscellaneous
Description: Tool collection for common and not so common data science use cases. This includes
custom made algorithms for data management as well as value calculations that are hard to find
elsewhere because of their specificity but would be a waste to get lost nonetheless.
sub-graphs in an edge list data.frame, find mode or modes in a vector of values, extract
(a) specific regular expression group(s), generate ISO time stamps that play well with
file names, or generate URL parameter lists by expanding value combinations.
Packaged: 2020-09-01 18:24:05 UTC; peter.meissner

Title: Estimation and Simulation of Drifting Semi-Markov Models
Description: Performs parametric and non-parametric estimation and simulation of
Drifting semi-Markov processes. The definition of parametric and non-parametric
model specifications is also possible. Furthermore, three different types of
Drifting semi-Markov models are considered. These models differ in the number
of transition matrices and sojourn time distributions used for the computation
of a number of semi-Markov kernels, which in turn characterize the Drifting
semi-Markov kernel. For the parametric model estimation and specification,
several discrete distributions are considered for the sojourn times: Uniform,
Poisson, Geometric, Discrete Weibull and Negative Binomial. The non-parametric
model specification makes no assumptions about the shape of the sojourn time
distributions. Semi-Markov models are described in:
Drifting Markov models are described in:
Reliability indicators of Drifting Markov models are described in:
Author: Vlad Stefan Barbu [aut] (<https://orcid.org/0000-0002-0840-016X>),