This post was prompted by this blog about using the cranlogs package by Gabor Csardi. But my own interest as long time package developer dates back to this post by Ben Bolker. I like to see that my packages are being used. So I thought why stop at counting downloads and plotting the past. Why not predict into the future?
I was invited to represent ABMI at the Multi-taxa Monitoring in North America symposium, North American Congress for Conservation Biology, Madison, Wisconsin, July 18, 2016. The symposium was organized by Michael Lucid (Idaho Department of Fish and Game). It was great to see all the good work happening in North America, and the commitment to push the agenda of multi-taxa monitoring against critics and scarce funding (of course Alberta ‘has all the oil money’).
As I was preparing for an R intro course I came up with the idea of creating a fake data set that is stuffed full of all the conceivable errors one can imagine. Just in case my imagination falls short, I’d appreciate all the suggestions in the comments so that I can incorporate more errors.
Automated acoustic monitoring is gaining momentum worldwide. Alberta is stepping up to the game by implementing automated recording unit (ARU) based monitoring programs. An improved command line tool is here to help in the process.
is a lightweight R extension package
that adds progress bar to vectorized R functions (
The latest addition in version 1.2-0
timerProgressBar function which adds a text based
progress bar with timer that all started with
this pull request.
The mefa4 R package is aimed at efficient manipulation of very big data sets leveraging sparse matrices thanks to the Matrix package. The recent update (version 0.3-3) of the package includes a bugfix and few new functions to compare sets and finding dominant features in compositional data as described in the ChangeLog.
Data cloning and MCMC tools for maximum likelihood methods
Hierarchical models made easy with data cloning
Analyzing wildlife data with detection error
Resource selection (probability) functions for use-availability data
Population viability analysis with data cloning
Models and data sets for the study of species-area relationships
Multivariate data handling in ecology and biogeography
Multivariate data handling with S4 classes and sparse matrices
Adding progress bar to '*apply' functions
Community ecology package
Mahon, C. L., Holloway, G., Sólymos, P., Cumming, S. G., Bayne, E. M., Schmiegelow, F. K. A., and Song, S. J., 2016. Community structure and niche characteristics of upland and lowland western boreal birds at multiple spatial scales. Forest Ecology and Management, 361:99–116. — journal website.
Bayne, E., Leston, L., Mahon, C. L., Sólymos, P., Machtans, C., Lankau, H., Ball, J., Van Wilgenburg, S., Cumming, S. G., Fontaine, T., Schmiegelow, F. K. A., and Song, S. J., 2016. Boreal bird abundance estimates within different energy sector disturbances vary with point count radius. Condor, 118:376–390. — journal website — fulltext PDF.
Sólymos, P., and Lele, S. R., 2016. Revisiting resource selection probability functions and single-visit methods: clarification and extensions. Methods in Ecology and Evolution, 7:196–205. — journal website — fulltext PDF —
detect R package — GitHub site.
Sólymos, P., Morrison, S. F., Kariyeva, J., Schieck, J., Haughland, D. L., Azeria, E., Cobb, T., Hinchliffe, R., Kittson, J., McIntosh, A., Narwani, T., Pierossi, P., Roy, M.-C., Sandybayev, T., Boutin, S., and Bayne, E., 2015. Data and information management for the monitoring of biodiversity in Alberta. Wildlife Society Bulletin, 39:472–479. — journal website.
Barker, N. K. S., Fontaine, P. C., Cumming, S. G., Stralberg, D., Westwood, A., Bayne, E. M., Sólymos, P., Schmiegelow, F. K. A., Song, S. J., and Rugg, D. J., 2015. Ecological monitoring through harmonizing existing data: lessons from the Boreal Avian Modelling Project. Wildlife Society Bulletin, 39:480–487. — journal website.