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unmarked (version 1.5.0)

Models for Data from Unmarked Animals

Description

Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) , Fiske and Chandler (2011) .

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Version

Install

install.packages('unmarked')

Monthly Downloads

6,921

Version

1.5.0

License

GPL (>= 3)

Homepage

https://groups.google.com/d/forum/unmarked

Maintainer

Ken Kellner

Last Published

February 10th, 2025

Functions in unmarked (1.5.0)

Compute Sum of Squared Residuals for a Model Fit.

backTransform-methods

Methods for Function backTransform in Package `unmarked'

confint-methods

Methods for Function confint in Package `unmarked'

computeMPLElambda

Compute the penalty weight for the MPLE penalized likelihood method

Open population model for distance sampling data

Detection/non-detection data on the European crossbill (Loxia curvirostra)

Cross-validation methods for fitted unmarked models and fit lists

Methods for bracket extraction [ in Package `unmarked'

Methods for Function coef in Package `unmarked'

Create a list of fitted unmarked models

Fit the dynamic occupancy model of MacKenzie et. al (2003)

Fit the combined distance and removal model of Amundson et al. (2014).

Calculate fitted (expected) values from a model

Convert between wide and long data formats.

Landscape data for Santa Cruz Island

Convert Poisson mean (lambda) to probability of occurrence (psi).

linearComb-methods

Methods for Function linearComb in Package `unmarked'

2001 Delaware North American Amphibian Monitoring Program Data

Bin distance data

Convert .CSV File to an unmarkedFrame

Get detection probability matrix from a fitted model

Fit the hierarchical distance sampling model of Royle et al. (2004)

Distance-sampling detection functions and associated density functions

Green frog count index data

Methods for Function getB in Package `unmarked'

Methods for Function getFP in Package `unmarked'

Generalized multinomial N-mixture model

Fit the multi-scale occupancy model of Nichols et al. (2008)

Get non-parametric bootstrap samples from an unmarked model

Model selection on a list of unmarked model fits

Fit the MacKenzie et al. (2002) Occupancy Model

Create unmarkedMultFrame from Long Format Data Frame

Generalized binomial N-mixture model for repeated count data

Multinomial-Poisson Mixtures Model

Fit the generalized distance sampling model of Chandler et al. (2011).

A function to impute missing entries in continuous obsCovs

Open population multinomial N-mixture model

Fit the N-mixture Time-to-detection Model of Strebel et al. (2021)

Distance-sampling data for the Island Scrub Jay (Aphelocoma insularis)

Simulated line transect data

European Jay data from the Swiss Breeding Bird Survey 2002

Mallard count data

Massachusetts North American Amphibian Monitoring Program Data

Create functions to compute multinomial cell probabilities

Fit the MacKenzie et al. (2002) Occupancy Model with the penalized likelihood methods of Hutchinson et al. (2015) using cross-validation

Fit the multi-species occupancy model of Rota et al. (2016)

Fit Single-Season and Dynamic Time-to-detection Occupancy Models

Fit the MacKenzie et al. (2002) Occupancy Model with the penalized likelihood methods of Hutchinson et al. (2015)

Fit the occupancy model of Royle and Nichols (2003)

optimizePenalty-methods

Identify Optimal Penalty Parameter Value

Fit occupancy models when false positive detections occur (e.g., Royle and Link [2006] and Miller et al. [2011])

Fit a community occupancy model with species-level random effects

Fit the occupancy model using count data

Parametric bootstrap for unmarked models

Removal data for the Ovenbird

Plot marginal effects of covariates in unmarked models

Simulated point-transect data

Fit the open N-mixture models of Dail and Madsen (2011) and Hostetler and Chandler (2015)

Compute multinomial cell probabilities

Estimate posterior distributions of site richness

Launch a Shiny app to help with power analysis

Simulate datasets from any unmarked model type

unmarked-package

Models for Data from Unmarked Animals

Predict from fitted models and other unmarked objects

unmarkedFrame-class

Class "unmarkedFrame"

Fit the N-mixture model of Royle (2004)

Create an unmarkedFrame, or one of its child classes.

Estimate posterior distributions of latent occupancy or abundance

Convert sight distance and sight angle to perpendicular distance.

Fit spatial hierarchical distance sampling model.

Calculate residuals from a model

Fit Multi-State Occupancy Models (Nichols et al. 2007, MacKenzie et al. 2009)

Extract estimates of random effect terms

posteriorSamples

Draw samples from the posterior predictive distribution

unmarkedFrameDS

Organize data for the distance sampling model of Royle et al. (2004) fit by distsamp

unmarkedEstimate-class

Class "unmarkedEstimate"

unmarkedFrameGDR

Organize data for the combined distance and removal point-count model of Amundson et al. (2014) fit by gdistremoval

Extract estimates of random effect standard deviations

unmarkedEstimateList-class

Class "unmarkedEstimateList"

Conduct a power analysis for an unmarked model

unmarkedFrameMMO

Create an object of class unmarkedFrameMMO that contains data used by multmixOpen.

unmarkedFrameDSO

Create an object of class unmarkedFrameDSO that contains data used by distsampOpen.

unmarkedFrameMPois

Organize data for the multinomial-Poisson mixture model of Royle (2004) fit by multinomPois

unmarkedFrameOccuCOP

Organize data for the occupancy model using count data fit by occuCOP

unmarkedFrameOccuComm

Organize data for the community occupancy model fit by occuComm

unmarkedPowerList

Summarize a series of unmarked power analyses

unmarkedPower-methods

Methods for unmarkedPower objects

unmarkedFrameOccu

Organize data for the single season occupancy models fit by occu and occuRN

Compute Variance Inflation Factors for an unmarkedFit Object.

unmarkedFrameOccuFP

Organize data for the single season occupancy models fit by occuFP

unmarkedFrameOccuMS

Organize data for the multi-state occupancy model fit by occuMS

unmarkedModSel-class

unmarkedModSel class and methods

unmarkedFrameOccuTTD

Create an unmarkedFrameOccuTTD object for the time-to-detection model fit by occuTTD

unmarkedFrameOccuMulti

Organize data for the multispecies occupancy model fit by occuMulti

unmarkedRanef-class

Class "unmarkedRanef"

unmarkedFramePCount

Organize data for the N-mixture model fit by pcount

unmarkedFramePCO

Create an object of class unmarkedFramePCO that contains data used by pcountOpen.

Methods for Function vcov in Package `unmarked'

unmarkedFitList-class

Class "unmarkedFitList"

unmarkedFit-class

Class "unmarkedFit"

unmarkedMultFrame

Create an unmarkedMultFrame, unmarkedFrameGMM, unmarkedFrameGDS, or unmarkedFrameGPC object

Methods for Function SE in Package `unmarked'

Swiss landscape data

BBS Point Count and Occurrence Data from 2 Bird Species

Extract Best Unbiased Predictors (BUPs) of latent variables from ranef output

Fit the integrated distance sampling model of Kery et al. (2022).

Occupancy data for coyote, red fox, and bobcat