Colin J. Carlson, PhD

An R package for estimating affiliate species richness based on power law scaling with host diversity, using rarefaction on bipartite species association networks. Because some things just go together. Use the function copredict to extrapolate power law curves out to a higher value. Use copredict.ci to fit a series of models to only half of the total curve, and see what happens (for an overestimated confidence bound).

Faculty: Colin J. Carlson, PhD

Download: GitHub / codependent package

Platform: R

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"The goal of this project is to come up with a reasonable way to predict pathogen sharing patterns between countries, using the DAME (dynamic additive and multiplicative effects model) for dyadic networks.

This follows on two previous preprints:
Poisot, T., Nunn, C., & Morand, S. (2014). Ongoing worldwide homogenization of human pathogens. bioRxiv, 009977.
Dallas, T. A., Carlson, C. J., & Poisot, T. (2018). Leveraging pathogen community distributions to understand outbreak and emergence potential. bioRxiv, 336065.
The GIDEON data is available as a list of countries, years, and pathogens. The goal is currently to come up with appropriate dyadic predictors for pairs of countries."

Faculty: Colin J. Carlson, PhD

Download: GitHub / dame-gideon package

Platform: R

Reference: doi.org (dame-gideon)

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Bayesian joint species distribution models with networks.

Faculty: Colin J. Carlson, PhD

Download: GitHub / elton package

Platform: R

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This package is basically a wrapper around 'dbarts' with a few tools: basic model summary statistics and diagnostics, spatial prediction with raster data, credible interval draws from the posterior distribution, visualization of how posterior draws learn over time, variable importance measures and plots, stepwise variable elimination, automatic Nice Plots for partials, including multiple ways to visualize posterior draws spatial projection of partials (""spartials""), compatibility with random intercept BART models (riBART), plots for random intercepts

In future versions I'd hope to include compatibility with: explicitly-spatial adaptations of BART (spatial priors), compatibility with smoothed BART models (softBART) and sparse BART models with Dirichlet priors (DART)"

Faculty: Colin J. Carlson, PhD

Download: GitHub / embarcadero package

Platform: R

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epizootic is an extension to poems, a spatially-explicit, process-explicit, pattern-oriented framework for modeling population dynamics. This extension adds functionality for modeling disease dynamics in wildlife. It also adds capability for seasonality and for unique dispersal dynamics for each life cycle stage.

Faculty: Colin J. Carlson, PhD

Download: Cran R / epizootic package

Platform: R

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David Onstad provided us with this insect disease database, sometimes referred to as the 'Ecological Database of the Worlds Insect Pathogens' or EDWIP. Files have been converted from 'SQL' to csv, and ported into 'R' for easy exploration and analysis. Thanks to the Macroecology of Infectious Disease Research Coordination Network (RCN) for funding and support. Data are also served online in a static format at <https://edwip.ecology.uga.edu/>;.

Faculty: Colin J. Carlson, PhD

Download: insectDisease package

Platform: R

Reference: doi.org (insectDisease)

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A template for easily creating pretty, nicely formatted preprints in LaTeX.

Faculty: Colin J. Carlson, PhD

Download: GitHub / LaPreprint package

Platform: LaTeX

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NCBITaxonomy.jl is a Julia package designed to address the complex challenges of taxonomic name reconciliation using a local copy of the NCBI taxonomic backbone (Federhen in Nucleic Acids Res 40:D136–D143, 2012, Schoch et al. in Database 2020:baaa062, 2020). The package provides advanced name matching capabilities that handle common issues in taxonomic data, including synonyms, homonyms, vernacular names, nomenclatural changes, and typographical errors.

Faculty: Colin J. Carlson, PhD

Download: GitHub / NCBITaxonomy package

Platform: Julia

Reference: doi.org (NCBITaxonomy)

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Every single system of notification has an intrinsic delay between the date of onset of the event and the date of report. nowcaster can estimate how many counts of any epidemiological data of interest (i.e., daily cases and deaths counts) by fitting a negative binomial model to the time steps of delay between onset date of the event, (i.e., date of first symptoms for cases or date of occurrence of death) and the date of report (i.e., date of notification of the case or death).

Faculty: Colin J. Carlson, PhD

Download: GitHub / Nowcaster package

Platform: R

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"A range of tools to help you get more out of NVivo(tm). Thanks to the wonderful Wooey some of these tools are now available for use on our server at http://wooey.barraqda.org. The core of NVivotools its ability to convert qualitative research data (sources, nodes, coding, etc.) into and out of NVivo's proprietary format. Some reasons why you might want to do this include:

1. Freeing your work. Make your research data available to whomever you want (including your future self), not only those with their own current NVivo license.
2. Choose the tools you want to manipulate your data. NVivo's GUI isn't bad, but sometimes you'd prefer to be able to automate. Use some of the plethora of data management tools or your own coding skills to take charge of your data.
3. Interface with the rest of your IT world. Make NVivo part of your toolkit, not your whole world.

The core of NVivotools is its ability to make sense of NVivo's proprietary file structures. These files are, in face, relational database. The Windows version uses Microsoft SQL Server while the Mac version uses SQL Anywhere. NVivotools is able to minimise the difficulties of working with different database engines by using SQLAlchemy."

Faculty: Colin J. Carlson, PhD

Download: GitHub / NVivotools package

Platform: Python

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This repository is part of the Pharos project which is split into three repositories: Repository Purpose, pharos-frontend: Frontend application and deployment infrastructure, pharos-api: API and deployment infrastructure, pharos-database: SQL database and deployment infrastructure, pharos-documentation: Markdown files used to generate about pages

Faculty: Colin J. Carlson, PhD

Download: GitHub / pharos-api package

Platform: Python

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Tools for making Plasmodium maps.

Faculty: Colin J. Carlson, PhD

Download: GitHub / plasma package

Platform: R

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Consistent and ergonomic methods for accessing versioned Virion data

Faculty: Colin J. Carlson, PhD

Download: GitHub / py-virion-data package

Platform: Python

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A new package that tracks species range shifts. It's still super in development so don't expect anything here for a while. Drop me a line if you're interested in helping somehow. (Dave Matthews voice) ants marching

Faculty: Colin J. Carlson, PhD

Download: GitHub / rangeshifts package

Platform: R

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The goal of this repository is to provide a reproducible R modelling pipeline that runs an analysis geared towards a Bayesian statistical modeling framework, using the targets package for reproducible workflows. The pipeline is designed to be run in a Docker container, which can be executed locally or on a high-performance computing (HPC) cluster. It is only Bayesian in the sense that it uses rstanarm for model fitting, but the pipeline can be adapted to other modelling frameworks like NIMBLE or brms. Rstan/rstanarm is used here as an example since it requires a C++ compiler and is a common choice for Bayesian modelling in R. More on that later.

Faculty: Colin J. Carlson, PhD

Download: GitHub / r-hpc-reproducible-repo package

Platform: R

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The repository provides a template for reproducible R projects. It uses {targets} to manage code execution (among other things) and {renv} to manage packages. Compute environments are managed via docker and continuous integration happens in github actions.

Faculty: Colin J. Carlson, PhD

Download: GitHub / r-reproducible-repo package

Platform: R

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This repository contains a standalone Python pipeline for assembling segmented Hantavirus genomes from Nanopore sequencing data. The pipeline performs quality control, mapping, consensus sequence generation, and report creation. An optional masking step is also available (requires a bed file containing primer sequences and coordinates, to date, it has not been implemented).

Faculty: Colin J. Carlson, PhD

Download: GitHub / SNVler package

Platform: Python

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Transcriptome Assembly, Thinning, and Annotation Tool

Faculty: Colin J. Carlson, PhD

Download: GitHub / tatat package

Platform: Python

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The goal of virionData is to provide a consistent and ergonomic way to access versions of the VIRION data set stored in Zenodo. This package allows users to select specific versions of the data set to work with, provides easy access to metadata, and allows users to export metadata. The pipeline for creating virion can be found at https://github.com/viralemergence/virion

Faculty: Colin J. Carlson, PhD

Download: virionData package

Platform: R

Reference: doi.org (insectDisease)

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This is an R package for validating data against the Wildlife Disease Data Standard. It allows users to restructure and validate data sets.

Faculty: Colin J. Carlson, PhD

Download: GitHub / wddsWizard package

Platform: R

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Some convenience tools for BART modeling

Faculty: Colin J. Carlson, PhD

Download: GitHub / westbound package

Platform: R

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