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marked (version 1.2.8)

Mark-Recapture Analysis for Survival and Abundance Estimation

Description

Functions for fitting various models to capture-recapture data including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and the multi-variate state model structure for survival estimation and POPAN structured Jolly-Seber models for abundance estimation. There are also Hidden Markov model (HMM) implementations of CJS and multistate models with and without state uncertainty and a simulation capability for HMM models.

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Version

Install

install.packages('marked')

Monthly Downloads

903

Version

1.2.8

License

GPL (>= 2)

Maintainer

Jeff Laake

Last Published

October 19th, 2023

Functions in marked (1.2.8)

compute_matrices

Compute HMM matrices
cjs_gamma

HMM Transition matrix functions
convert.link.to.real

Convert link values to real parameters
cjs_delta

HMM Initial state distribution functions
cjs_tmb

Fitting function for CJS models
create.dmdf

Creates a dataframe with all the design data for a particular parameter in a crm model
coef.crm

Extract coefficients
create.fixed.matrix

Create parameters with fixed matrix
compute_real

Compute estimates of real parameters
create.dm

Creates a design matrix for a parameter
dipper

Dipper capture-recapture data
global_decode

Global decoding of HMM
fix.parameters

Fixing real parameters in crm models
create.links

Creates a 0/1 vector for real parameters with sin link
crm.wrapper

Automation of model runs
crm

Capture-recapture model fitting function
function.wrapper

Utility extract functions
deriv_inverse.link

Derivatives of inverse of link function (internal use)
dmat_hsmm2hmm

Create expanded state-dependent observation matrix for HMM from HSMM
hmmDemo

HMM computation demo functions
js.hessian

Compute variance-covariance matrix for fitted JS model
js.lnl

Likelihood function for Jolly-Seber model using Schwarz-Arnason POPAN formulation
make.design.data

Create design dataframes for crm
hsmm2hmm

Compute transition matrix for HMM from HSMM
js.accumulate

Accumulates common capture history values
js

Fitting function for Jolly-Seber model using Schwarz-Arnason POPAN formulation
local_decode

Local decoding of HMM
merge_design.covariates

Merge time (occasion) and/or group specific covariates into design data
inverse.link

Inverse link functions (internal use)
initiate_pi

Setup fixed values for pi in design data
mstrata

Multistrata example data
mscjs_tmb

Fitting function for Multistate CJS models with TMB
mixed.model.admb

Mixed effect model contstruction
mvms_design_data

Multivariate Multistate (mvms) Design Data
mvmscjs_tmb

TMB version: Fitting function for Multivariate Multistate CJS with uncertainty models
msld_tmb

Fitting function for Multistate CJS live-dead models with TMB
mscjs

Fitting function for Multistate CJS models
mvmscjs

Fitting function for Multivariate Multistate CJS with uncertainty models
mvms_dmat

HMM Observation Probability matrix functions
omega

Compute 1 to k-step transition proportions
predict.crm

Compute estimates of real parameters
proc.form

Mixed effect model formula parser Parses a mixed effect model in the lme4 structure of ~fixed +(re1|g1) +...+(ren|gn)
probitCJS

Perform MCMC analysis of a CJS model
process.ch

Process release-recapture history data
process.data

Process encounter history dataframe for MARK analysis
sealions

Multivariate State example data
print.crm

Print model results
set.fixed

Set fixed real parameter values in ddl
print.crmlist

Print model table from model list
resight.matrix

Various utility functions
set.initial

Set initial values
set_scale

Scaling functions
smsld_tmb

Fitting function for Multistate CJS live-dead models with TMB
splitCH

Split/collapse capture histories
skagit

An example of the Mulstistrata (multi-state) model in which states are routes taken by migrating fish.
set_mvms

Multivariate Multistate (mvms) Specification
setup_tmb

TMB setup
simHMM

Simulates data from Hidden Markov Model
valid.parameters

Determine validity of parameters for a model (internal use)
setup_admb

ADMB setup
setup.model

Defines model specific parameters (internal use)
setup.parameters

Setup parameter structure specific to model (internal use)
tagloss

Tag loss example
cjs.initial

Computes starting values for CJS p and Phi parameters
cjs.hessian

Compute variance-covariance matrix for fitted CJS model
HMMLikelihood

Hidden Markov Model likelihood functions
cjs.lnl

Likelihood function for Cormack-Jolly-Seber model
backward_prob

Computes backward probabilities
cjs_admb

Fitting function for CJS models
R_HMMLikelihood

Hidden Markov Model Functions
Phi.mean

Various utility parameter summary functions
cjs.accumulate

Accumulates common capture history values
Paradise_shelduck

Mulstistate Live-Dead Paradise Shelduck Data