Title: Calculates Study Size Required for Distance Sampling
Description: Calculates the study size (either number of detections, or
proportion of region that should be covered) to achieve a target precision for
the estimated abundance. The calculation allows for the penalty due to unknown
detection function, and for overdispersion. The user must specify a guess at the

Authors@R: c(person("Fredrik", "Savje", email = "fredrik.savje@berkeley.edu", role = c("aut", "cre")))
Description: Provides tools for constructing, manipulating and using distance metrics.
LicenseNote: The distances packages includes the ANN library
(distributed under the LGPLv2.1 license).

X-CRAN-Comment: Orpahned on 2018-07-10 as maintainer address bounced.

Authors@R: c(person("Fredrik", "Savje", email = "fredrik.savje@yale.edu", role = c("aut", "cre")))

Packaged: 2018-12-12 00:31:48 UTC; fredriksavje

Packaged: 2018-12-12 21:08:19 UTC; fredriksavje

X-CRAN-Comment: Orphaned on 2019-09-01 as deprecated use of C++98 was

Packaged: 2019-03-18 05:27:33 UTC; fredriksavje

Description: Calculates distances from point locations to features.
The usual approach for eg. resource selection function analyses is to
generate a complete distance to features surface then sample it with your
observed and random points. Since these raster based approaches can be
pretty costly with large areas, and often lead to memory issues in R,
the distanceto package opts to compute these distances using
efficient, vector based approaches. As a helper, there's a decidedly
low-res raster based approach for visually inspecting your region's
distance surface. But the workhorse is distance_to.
Suggests: fasterize, knitr, rmarkdown, spData, raster, tinytest
Author: Alec L. Robitaille [aut, cre] (<https://orcid.org/0000-0002-4706-1762>)
Maintainer: Alec L. Robitaille <robit.alec@gmail.com>

Title: Assessing Dissimilarity Between Multivariate Time Series
Maintainer: Blas M. Benito <blasbenito@gmail.com>
Description: Provides tools to assess the dissimilarity between multivariate time-series. It is based on the psi measure described in "Numerical methods in Quaternary pollen analysis" (Birks and Gordon, 1985 <doi:10.1002/jqs.3390020110>), which computes dissimilarity constrained by sample order, but the original idea has been extended to work with any kind of multivariate time-series, no matter whether they are regular, irregular, aligned or unaligned. Furthermore, the package allows to assess the significance of dissimilarity values by applying a restricted permutation test, allows to measure the contribution of individual variables to dissimilarity, and offers tools to transfer attributes (generally time or age, but other are possible) between sequences based on the similarity of their samples.
Imports: plyr, grDevices, foreach, parallel, doParallel, fields,
Suggests: devtools, formatR, kableExtra, magrittr, knitr, rmarkdown