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distancedevelopment

Distance:Distance Sampling Detection Function and Abundance Estimation

A simple way of fitting detection functions to distance sampling data for both line and point transects. Adjustment term selection, left and right truncation as well as monotonicity constraints and binning are supported. Abundance and density estimates can also be calculated (via a Horvitz-Thompson-like estimator) if survey area information is provided. See Miller et al. (2019) <doi:10.18637/jss.v089.i01> for more information on methods and <https://examples.distancesampling.org/> for example analyses.

Maintained by Laura Marshall. Last updated 17 days ago.

11 stars 8.96 score 358 scripts 3 dependents

molina-valero

FORTLS:Automatic Processing of Terrestrial-Based Technologies Point Cloud Data for Forestry Purposes

Process automation of point cloud data derived from terrestrial-based technologies such as Terrestrial Laser Scanner (TLS) or Mobile Laser Scanner. 'FORTLS' enables (i) detection of trees and estimation of tree-level attributes (e.g. diameters and heights), (ii) estimation of stand-level variables (e.g. density, basal area, mean and dominant height), (iii) computation of metrics related to important forest attributes estimated in Forest Inventories at stand-level, and (iv) optimization of plot design for combining TLS data and field measured data. Documentation about 'FORTLS' is described in Molina-Valero et al. (2022, <doi:10.1016/j.envsoft.2022.105337>).

Maintained by Juan Alberto Molina-Valero. Last updated 15 days ago.

forest-inventoryforest-monitoringlidar-point-cloudcpp

23 stars 6.48 score 11 scripts

distancedevelopment

dsm:Density Surface Modelling of Distance Sampling Data

Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.

Maintained by Laura Marshall. Last updated 2 years ago.

8 stars 6.09 score 146 scripts

distancedevelopment

dsims:Distance Sampling Simulations

Performs distance sampling simulations. 'dsims' repeatedly generates instances of a user defined population within a given survey region. It then generates realisations of a survey design and simulates the detection process. The data are then analysed so that the results can be compared for accuracy and precision across all replications. This process allows users to optimise survey designs for their specific set of survey conditions. The effects of uncertainty in population distribution or parameters can be investigated under a number of simulations so that users can be confident that they have achieved a robust survey design before deploying vessels into the field. The distance sampling designs used in this package from 'dssd' are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 978-0199225873). General distance sampling methods are detailed in Introduction to Distance Sampling: Estimating Abundance of Biological Populations, Buckland et. al. (2004, ISBN-13: 978-0198509271). Find out more about estimating animal/plant abundance with distance sampling at <https://distancesampling.org/>.

Maintained by Laura Marshall. Last updated 2 months ago.

2 stars 4.93 score 71 scripts

distancedevelopment

mads:Multi-Analysis Distance Sampling

Performs distance sampling analyses on a number of species at once and can account for unidentified sightings, model uncertainty and covariate uncertainty. Unidentified sightings refer to sightings which cannot be allocated to a single species but may instead be allocated to a group of species. The abundance of each unidentified group is estimated and then prorated to the species estimates. Model uncertainty should be incorporated when multiple models give equally good fit to the data but lead to large differences in estimated density / abundance. Covariate uncertainty should be incorporated when covariates cannot be measured accurately, for example this is often the case for group size in marine mammal surveys. Variance estimation for these methods is via a non parametric bootstrap. The methods implemented are described in Gerodette T. and Forcada J. (2005) <10.3354/meps291001> Non-recovery of two spotted and spinner dolphin populations in the eastern tropical Pacific Ocean.

Maintained by Laura Marshall. Last updated 1 years ago.

2 stars 2.30 score 2 scripts

distancedevelopment

readdst:Convert Distance for Windows projects to R analyses

Take projects built using Distance for Windows and create R scripts which duplicate the analysis. Optionally build a test suite that checks analysis results from Distance with the equivalent R results.

Maintained by David Miller. Last updated 4 years ago.

1 stars 1.78 score 12 scripts

dsjohnson

varpropTMB:What the Package Does (One Line, Title Case)

What the package does (one paragraph).

Maintained by First Last. Last updated 2 months ago.

1.48 score

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