What is it?

We develop specialized statistical approaches to harmonize different datasets by correcting for survey methodology and species detectability. 

 

Compiling and analyzing avian data from across North America is challenging because data is collected in a variety of ways. We have developed specialized statistical approaches to harmonize different datasets by correcting for survey methodology and species detectability. These methods are intended for researchers who have to analyze multiple field observations and who are often confronted with data heterogeneity due to variability in sampling protocols and detection errors. These methods are also suitable for researchers trying to combine ‘legacy’ datasets with new data collected by acoustic recording units or point counts.

R Packages

BAM develops and contributes to R packages for analyzing avian data. Explain more about what R packages are. Lorem ipsum dolor sit amet, consectetur adipiscing elit.

QPAD: Calibrating indices of avian density from non-standardized survey data

The analysis of large heterogeneous data sets of avian point-count surveys compiled across studies is hindered by a lack of analytical approaches that can…
 

R Package

lhreg: phylogeny and species trait effects on detectability

The lhreg R extension package is a supporting material for the manuscript by Solymos et al. (2018).  The package contains the (1) data, (2) analysis code used in the manuscript, and (3) code required to summarize the results and produce tables and figures.

R Package

paired:  example point count data set with paired human and ARU data

Example point count data set with paired human and ARU data

R Package

detect: for analyzing wildlife data with detection error

Models for analyzing site occupancy and count data models with detection error, including single-visit based models, conditional distance sampling and time-removal models.

R Package

bSims – bird point count simulator

A highly scientific and utterly addictive bird point count simulator to test statistical assumptions, aid survey design, and have fun while doing it.

R Package

cure4insect: custom reporting for intactness and sector effects.

Custom Reporting for Intactness and Sector Effects

R Package

mefa4: multivariate data handling with S4 classes and sparse matrices

An S4 update of the ‘mefa’ package using sparse matrices for enhanced efficiency. Sparse array-like objects are supported via lists of sparse matrices.

R Package