<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>robitalec.r-universe.dev</title><link>https://robitalec.r-universe.dev</link><description>Recent package updates in robitalec</description><generator>R-universe</generator><image><url>https://github.com/robitalec.png</url><title>R packages by robitalec</title><link>https://robitalec.r-universe.dev</link></image><lastBuildDate>Fri, 06 Mar 2026 15:06:33 GMT</lastBuildDate><item><title>[ropensci] spatsoc 0.2.12.9010</title><author>robit.alec@gmail.com (Alec L. Robitaille)</author><description>Detects spatial and temporal groups in GPS relocations
(Robitaille et al. (2019) &lt;doi:10.1111/2041-210X.13215&gt;).  It
can be used to convert GPS relocations to gambit-of-the-group
format to build proximity-based social networks In addition,
the randomizations function provides data-stream randomization
methods suitable for GPS data.</description><link>https://github.com/r-universe/ropensci/actions/runs/23998630086</link><pubDate>Fri, 06 Mar 2026 15:06:33 GMT</pubDate><r:package>spatsoc</r:package><r:version>0.2.12.9010</r:version><r:status>success</r:status><r:repository>https://ropensci.r-universe.dev</r:repository><r:upstream>https://github.com/ropensci/spatsoc</r:upstream><r:article><r:source>additional-data-formats.Rmd</r:source><r:filename>additional-data-formats.html</r:filename><r:title>Detecting interspecific interactions</r:title><r:created>2023-09-08 13:13:58</r:created><r:modified>2026-03-06 15:05:24</r:modified></r:article><r:article><r:source>faq.Rmd</r:source><r:filename>faq.html</r:filename><r:title>Frequently asked questions about spatsoc</r:title><r:created>2018-07-28 01:28:28</r:created><r:modified>2026-03-06 15:05:24</r:modified></r:article><r:article><r:source>intro.Rmd</r:source><r:filename>intro.html</r:filename><r:title>Introduction to spatsoc</r:title><r:created>2025-07-29 14:59:20</r:created><r:modified>2025-12-19 15:50:15</r:modified></r:article><r:article><r:source>geometry-interface-and-spatial-measures.Rmd</r:source><r:filename>geometry-interface-and-spatial-measures.html</r:filename><r:title>New geometry interface and spatial measures</r:title><r:created>2025-12-19 15:50:15</r:created><r:modified>2026-03-06 15:05:24</r:modified></r:article><r:article><r:source>using-edge-and-dyad.Rmd</r:source><r:filename>using-edge-and-dyad.html</r:filename><r:title>Using distance based edge-list generating functions, dyad_id and fusion_id</r:title><r:created>2020-03-25 23:50:45</r:created><r:modified>2025-12-19 15:50:15</r:modified></r:article><r:article><r:source>using-in-sna.Rmd</r:source><r:filename>using-in-sna.html</r:filename><r:title>Using spatsoc in social network analysis</r:title><r:created>2018-09-13 18:31:00</r:created><r:modified>2025-12-19 15:50:15</r:modified></r:article></item><item><title>[robitalec] spatsoc 0.2.12.9010</title><author>robit.alec@gmail.com (Alec L. Robitaille)</author><description>Detects spatial and temporal groups in GPS relocations
(Robitaille et al. (2019) &lt;doi:10.1111/2041-210X.13215&gt;).  It
can be used to convert GPS relocations to gambit-of-the-group
format to build proximity-based social networks In addition,
the randomizations function provides data-stream randomization
methods suitable for GPS data.</description><link>https://github.com/r-universe/robitalec/actions/runs/23998619251</link><pubDate>Fri, 06 Mar 2026 15:06:33 GMT</pubDate><r:package>spatsoc</r:package><r:version>0.2.12.9010</r:version><r:status>success</r:status><r:repository>https://robitalec.r-universe.dev</r:repository><r:upstream>https://github.com/ropensci/spatsoc</r:upstream><r:article><r:source>additional-data-formats.Rmd</r:source><r:filename>additional-data-formats.html</r:filename><r:title>Detecting interspecific interactions</r:title><r:created>2023-09-08 13:13:58</r:created><r:modified>2026-03-06 15:05:24</r:modified></r:article><r:article><r:source>faq.Rmd</r:source><r:filename>faq.html</r:filename><r:title>Frequently asked questions about spatsoc</r:title><r:created>2018-07-28 01:28:28</r:created><r:modified>2026-03-06 15:05:24</r:modified></r:article><r:article><r:source>intro.Rmd</r:source><r:filename>intro.html</r:filename><r:title>Introduction to spatsoc</r:title><r:created>2025-07-29 14:59:20</r:created><r:modified>2025-12-19 15:50:15</r:modified></r:article><r:article><r:source>geometry-interface-and-spatial-measures.Rmd</r:source><r:filename>geometry-interface-and-spatial-measures.html</r:filename><r:title>New geometry interface and spatial measures</r:title><r:created>2025-12-19 15:50:15</r:created><r:modified>2026-03-06 15:05:24</r:modified></r:article><r:article><r:source>using-edge-and-dyad.Rmd</r:source><r:filename>using-edge-and-dyad.html</r:filename><r:title>Using distance based edge-list generating functions, dyad_id and fusion_id</r:title><r:created>2020-03-25 23:50:45</r:created><r:modified>2025-12-19 15:50:15</r:modified></r:article><r:article><r:source>using-in-sna.Rmd</r:source><r:filename>using-in-sna.html</r:filename><r:title>Using spatsoc in social network analysis</r:title><r:created>2018-09-13 18:31:00</r:created><r:modified>2025-12-19 15:50:15</r:modified></r:article></item><item><title>[robitalec] irg 0.1.6</title><author>robit.alec@gmail.com (Alec L. Robitaille)</author><description>Fits a double logistic function to NDVI time series and
calculates instantaneous rate of green (IRG) according to
methods described in Bischoff et al. (2012)
&lt;doi:10.1086/667590&gt;.</description><link>https://github.com/r-universe/robitalec/actions/runs/24598515446</link><pubDate>Sun, 10 Nov 2024 18:56:14 GMT</pubDate><r:package>irg</r:package><r:version>0.1.6</r:version><r:status>success</r:status><r:repository>https://robitalec.r-universe.dev</r:repository><r:upstream>https://github.com/robitalec/irg</r:upstream><r:article><r:source>example-scripts.Rmd</r:source><r:filename>example-scripts.html</r:filename><r:title>Example Earth Engine scripts</r:title><r:created>2021-12-02 00:45:56</r:created><r:modified>2022-09-28 15:42:55</r:modified></r:article><r:article><r:source>getting-started-with-irg.Rmd</r:source><r:filename>getting-started-with-irg.html</r:filename><r:title>Getting started with irg</r:title><r:created>2018-12-06 22:48:24</r:created><r:modified>2022-02-18 01:57:34</r:modified></r:article></item><item><title>[robitalec] camtrapmonitoring 0.12.1</title><author>robit.alec@gmail.com (Alec L. Robitaille)</author><description>Evaluating potential camera trap locations. Potential
locations are evaluated using relevant spatial layers producing
a dataset of selected locations with covariates that can be
used to quantify sampling bias. Soon - density estimation
methods.</description><link>https://github.com/r-universe/robitalec/actions/runs/23372412554</link><pubDate>Tue, 25 Jun 2024 14:02:04 GMT</pubDate><r:package>camtrapmonitoring</r:package><r:version>0.12.1</r:version><r:status>success</r:status><r:repository>https://robitalec.r-universe.dev</r:repository><r:upstream>https://github.com/robitalec/camtrapmonitoring</r:upstream><r:article><r:source>intro-camtrapmonitoring.Rmd</r:source><r:filename>intro-camtrapmonitoring.html</r:filename><r:title>Introduction to camtrapmonitoring</r:title><r:created>2023-07-25 14:15:51</r:created><r:modified>2024-01-27 15:50:57</r:modified></r:article></item><item><title>[robitalec] preparelocs 0.1.3</title><author>robit.alec@gmail.com (Alec L. Robitaille)</author><description>Prepares animal relocation datasets for the Wildlife
Evolutionary Ecology Lab at Memorial University.</description><link>https://github.com/r-universe/robitalec/actions/runs/23998653893</link><pubDate>Fri, 26 Jan 2024 15:57:51 GMT</pubDate><r:package>preparelocs</r:package><r:version>0.1.3</r:version><r:status>success</r:status><r:repository>https://robitalec.r-universe.dev</r:repository><r:upstream>https://github.com/robitalec/prepare-locs</r:upstream></item><item><title>[robitalec] CSEE.reproducible.workflows.workshop 0.0.0.9000</title><author>robit.alec@gmail.com (Alec L. Robitaille)</author><description>Developing a reproducible workflow in R using functions,
targets and renv.</description><link>https://github.com/r-universe/robitalec/actions/runs/23781704879</link><pubDate>Sun, 11 Jun 2023 15:08:25 GMT</pubDate><r:package>CSEE.reproducible.workflows.workshop</r:package><r:version>0.0.0.9000</r:version><r:status>success</r:status><r:repository>https://robitalec.r-universe.dev</r:repository><r:upstream>https://github.com/robitalec/2023-CSEE-reproducible-workflows-workshop</r:upstream></item><item><title>[robitalec] distanceto 0.0.3</title><author>robit.alec@gmail.com (Alec L. Robitaille)</author><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.</description><link>https://github.com/r-universe/robitalec/actions/runs/24118162977</link><pubDate>Thu, 01 Jun 2023 11:42:50 GMT</pubDate><r:package>distanceto</r:package><r:version>0.0.3</r:version><r:status>success</r:status><r:repository>https://robitalec.r-universe.dev</r:repository><r:upstream>https://github.com/robitalec/distance-to</r:upstream><r:article><r:source>intro.Rmd</r:source><r:filename>intro.html</r:filename><r:title>Introduction to distance-to</r:title><r:created>2021-07-02 03:44:24</r:created><r:modified>2023-05-31 16:54:30</r:modified></r:article></item><item><title>[robitalec] hwig 0.0.2</title><author>robit.alec@gmail.com (Alec L. Robitaille)</author><description>The half-weight index gregariousness (HWIG) is an
association index used in social network analyses. It extends
the half-weight association index (HWI), correcting for level
of gregariousness in individuals. It is calculated using group
by individual data according to methods described in Godde et
al. (2013) &lt;doi:10.1016/j.anbehav.2012.12.010&gt;.</description><link>https://github.com/r-universe/robitalec/actions/runs/23998663372</link><pubDate>Fri, 03 Mar 2023 22:16:25 GMT</pubDate><r:package>hwig</r:package><r:version>0.0.2</r:version><r:status>success</r:status><r:repository>https://robitalec.r-universe.dev</r:repository><r:upstream>https://github.com/robitalec/hwig</r:upstream></item><item><title>[robitalec] ScaleInMultilayerNetworks 0.1.1</title><author>robit.alec@gmail.com (Alec L. Robitaille)</author><description>Scale remains a foundational concept in ecology. Spatial
scale, for instance, has become a central consideration in the
way we understand landscape ecology and animal space use.
Meanwhile, scale-dependent social processes can range from
fine-scale interactions to co-occurrence and overlapping home
ranges. Furthermore, sociality can vary within and across
seasons. Multilayer networks promise the explicit integration
of the social, spatial and, temporal contexts. Given the
complex interplay of sociality and animal space use in
heterogeneous landscapes, there remains an important gap in our
understanding of the influence of scale on animal social
networks. Using an empirical case study, we discuss ways of
considering social, spatial and, temporal scale in the context
of multilayer caribou social networks. Effective integration of
social and spatial processes, including biologically meaningful
scales, within the context of animal social networks is an
emerging area of research. We incorporate perspectives that
link the social environment to spatial processes across scales
in a multilayer context.</description><link>https://github.com/r-universe/robitalec/actions/runs/23998650583</link><pubDate>Fri, 28 May 2021 20:45:51 GMT</pubDate><r:package>ScaleInMultilayerNetworks</r:package><r:version>0.1.1</r:version><r:status>success</r:status><r:repository>https://robitalec.r-universe.dev</r:repository><r:upstream>https://github.com/robitalec/ScaleInMultilayerNetworks</r:upstream></item><item><title>[robitalec] SocCaribou 0.1.0</title><author>robit.alec@gmail.com (Alec L. Robitaille)</author><description>Package Accompanying: Space-use and social organization in
a gregarious ungulate: testing the conspecific attraction and
resource dispersion hypotheses</description><link>https://github.com/r-universe/robitalec/actions/runs/23998656941</link><pubDate>Thu, 18 Apr 2019 22:36:35 GMT</pubDate><r:package>SocCaribou</r:package><r:version>0.1.0</r:version><r:status>success</r:status><r:repository>https://robitalec.r-universe.dev</r:repository><r:upstream>https://github.com/wildlifeevoeco/SocCaribou</r:upstream></item></channel></rss>