unmarked1.4.3 package

Models for Data from Unmarked Animals

backTransform-methods.rd

Methods for Function backTransform in Package `unmarked'

coef-methods

Methods for Function coef in Package `unmarked'

colext

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

computeMPLElambdas

Compute the penalty weight for the MPLE penalized likelihood method

confint-methods

Methods for Function confint in Package `unmarked'

crossVal

Cross-validation methods for fitted unmarked models and fit lists

csvToUMF

Convert .CSV File to an unmarkedFrame

detFuns

Distance-sampling detection functions and associated density functions

distsamp

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

distsampOpen

Open population model for distance sampling data

extract-methods

Methods for bracket extraction [ in Package `unmarked'

fitList

constructor of unmarkedFitList objects

fitted-methods

Methods for Function fitted in Package `unmarked'

formatDistData

Bin distance data

formatMult

Create unmarkedMultFrame from Long Format Data Frame

formatWideLong

Convert between wide and long data formats.

gdistremoval

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

gdistsamp

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

getB-methods

Methods for Function getB in Package `unmarked'

getFP-methods

Methods for Function getFP in Package `unmarked'

getP-methods

Methods for Function getP in Package `unmarked'

gmultmix

Generalized multinomial N-mixture model

goccu

Fit multi-scale occupancy models

gpcount

Generalized binomial N-mixture model for repeated count data

IDS

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

imputeMissing

A function to impute missing entries in continuous obsCovs

lambda2psi

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

linearComb-methods

Methods for Function linearComb in Package `unmarked'

makePiFuns

Create functions to compute multinomial cell probabilities

modSel

Model selection results from an unmarkedFitList

multinomPois

Multinomial-Poisson Mixtures Model

multmixOpen

Open population multinomial N-mixture model

nmixTTD

Fit N-mixture Time-to-detection Models

nonparboot-methods

Nonparametric bootstrapping in unmarked

occu

Fit the MacKenzie et al. (2002) Occupancy Model

occuCOP

Fit the occupancy model using count dta

occuFP

Fit occupancy models when false positive detections occur (e.g., Royle...

occuMS

Fit Single-Season and Dynamic Multi-State Occupancy Models

occuMulti

Fit the Rota et al. (2016) Multi-species Occupancy Model

occuPEN_CV

Fit the MacKenzie et al. (2002) Occupancy Model with the penalized lik...

occuPEN

Fit the MacKenzie et al. (2002) Occupancy Model with the penalized lik...

occuRN

Fit the occupancy model of Royle and Nichols (2003)

occuTTD

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

optimizePenalty-methods

Identify Optimal Penalty Parameter Value

parboot

Parametric bootstrap method for fitted models inheriting class.

pcount

Fit the N-mixture model of Royle (2004)

pcount.spHDS

Fit spatial hierarchical distance sampling model.

pcountOpen

Fit the open N-mixture models of Dail and Madsen and extensions

piFuns

Compute multinomial cell probabilities

plotEffects

Plot marginal effects of covariates in unmarked models

posteriorSamples

Draw samples from the posterior predictive distribution

powerAnalysis

Conduct a power analysis for an unmarked model

predict-methods

Methods for Function predict in Package `unmarked'

randomTerms

Extract estimates of random effect terms

ranef-methods

Methods for Function ranef in Package unmarked

SE-methods

Methods for Function SE in Package `unmarked'

shinyPower

Launch a Shiny app to help with power analysis

sight2perpdist

Convert sight distance and sight angle to perpendicular distance.

sigma

Extract estimates of random effect standard deviations

simulate-methods

Methods for Function simulate in Package `unmarked'

SSE

Compute Sum of Squared Residuals for a Model Fit.

unmarked-package

Models for Data from Unmarked Animals

unmarkedEstimate-class

Class "unmarkedEstimate"

unmarkedEstimateList-class

Class "unmarkedEstimateList"

unmarkedFit-class

Class "unmarkedFit"

unmarkedFitList-class.rd

Class "unmarkedFitList"

unmarkedFrame-class

Class "unmarkedFrame"

unmarkedFrame

Create an unmarkedFrame, or one of its child classes.

unmarkedFrameDS

Organize data for the distance sampling model of Royle et al. (2004) f...

unmarkedFrameDSO

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

unmarkedFrameGDR

Organize data for the combined distance and removal point-count model ...

unmarkedFrameMMO

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

unmarkedFrameMPois

Organize data for the multinomial-Poisson mixture model of Royle (2004...

unmarkedFrameOccu

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

unmarkedFrameOccuCOP

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

unmarkedFrameOccuFP

Organize data for the single season occupancy models fit by occuFP

unmarkedFrameOccuMS

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

unmarkedFrameOccuMulti

Organize data for the multispecies occupancy model fit by occuMulti

unmarkedFrameOccuTTD

Create an unmarkedFrameOccuTTD object for the time-to-detection model ...

unmarkedFramePCO

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

unmarkedFramePCount

Organize data for the N-mixture model fit by pcount

unmarkedMultFrame

Create an unmarkedMultFrame, unmarkedFrameGMM, unmarkedFrameGDS, or un...

unmarkedPower-class

Methods for unmarkedPower objects

unmarkedPowerList

Summarize a series of unmarked power analyses

unmarkedRanef-class

Class "unmarkedRanef"

vcov-methods

Methods for Function vcov in Package `unmarked'

vif

Compute Variance Inflation Factors for an unmarkedFit Object.

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) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

  • Maintainer: Ken Kellner
  • License: GPL (>= 3)
  • Last published: 2024-09-01