Mark-Recapture Analysis for Survival and Abundance Estimation

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Documentation for package ‘marked’ version 1.2.8

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A B C D F G H I J L M N O P R S T U V

-- A --

accumulate_data Process encounter history dataframe for MARK analysis

-- B --

backward_prob Computes backward probabilities

-- C --

cjs.accumulate Accumulates common capture history values
cjs.hessian Compute variance-covariance matrix for fitted CJS model
cjs.initial Computes starting values for CJS p and Phi parameters
cjs.lnl Likelihood function for Cormack-Jolly-Seber model
cjs_admb Fitting function for CJS models
cjs_delta HMM Initial state distribution functions
cjs_dmat HMM Observation Probability matrix functions
cjs_gamma HMM Transition matrix functions
cjs_tmb Fitting function for CJS models
coef.crm Extract coefficients
collapseCH Split/collapse capture histories
compute_matrices Compute HMM matrices
compute_real Compute estimates of real parameters
convert.link.to.real Convert link values to real parameters
create.dm Creates a design matrix for a parameter
create.dmdf Creates a dataframe with all the design data for a particular parameter in a crm model
create.dml Creates a design matrix for a parameter
create.fixed.matrix Create parameters with fixed matrix
create.links Creates a 0/1 vector for real parameters with sin link
create.model.list Automation of model runs
crm Capture-recapture model fitting function
crm.wrapper Automation of model runs
crmlist_fromfiles Automation of model runs

-- D --

deriv.inverse.link Derivatives of inverse of link function (internal use)
deriv_inverse.link Derivatives of inverse of link function (internal use)
dipper Dipper capture-recapture data
dmat_hsmm2hmm Create expanded state-dependent observation matrix for HMM from HSMM

-- F --

fix.parameters Fixing real parameters in crm models
function.wrapper Utility extract functions
fx.aic Utility extract functions
fx.par.count Utility extract functions

-- G --

global_decode Global decoding of HMM

-- H --

hmm.lnl Hidden Markov Model likelihood functions
hmmDemo HMM computation demo functions
HMMLikelihood Hidden Markov Model likelihood functions
hsmm2hmm Compute transition matrix for HMM from HSMM

-- I --

initiate_pi Setup fixed values for pi in design data
inverse.link Inverse link functions (internal use)

-- J --

js Fitting function for Jolly-Seber model using Schwarz-Arnason POPAN formulation
js.accumulate Accumulates common capture history values
js.hessian Compute variance-covariance matrix for fitted JS model
js.lnl Likelihood function for Jolly-Seber model using Schwarz-Arnason POPAN formulation

-- L --

load.model Automation of model runs
local_decode Local decoding of HMM
loglikelihood Hidden Markov Model Functions

-- M --

make.design.data Create design dataframes for crm
mcmc_mode Various utility functions
merge.design.covariates Merge time (occasion) and/or group specific covariates into design data
merge_design.covariates Merge time (occasion) and/or group specific covariates into design data
mixed.model Mixed effect model contstruction
mixed.model.admb Mixed effect model contstruction
mixed.model.dat Mixed effect model contstruction
model.table Automation of model runs
ms2_gamma HMM Transition matrix functions
mscjs Fitting function for Multistate CJS models
mscjs_tmb Fitting function for Multistate CJS models with TMB
msld_tmb Fitting function for Multistate CJS live-dead models with TMB
mstrata Multistrata example data
ms_dmat HMM Observation Probability matrix functions
ms_gamma HMM Transition matrix functions
mvmscjs Fitting function for Multivariate Multistate CJS with uncertainty models
mvmscjs_delta HMM Initial state distribution functions
mvmscjs_tmb TMB version: Fitting function for Multivariate Multistate CJS with uncertainty models
mvms_design_data Multivariate Multistate (mvms) Design Data
mvms_dmat HMM Observation Probability matrix functions

-- N --

naive.survival Various utility functions

-- O --

omega Compute 1 to k-step transition proportions

-- P --

p.boxplot Various utility parameter summary functions
p.mean Various utility parameter summary functions
Paradise_shelduck Mulstistate Live-Dead Paradise Shelduck Data
Phi.boxplot Various utility parameter summary functions
Phi.mean Various utility parameter summary functions
predict.crm Compute estimates of real parameters
print.crm Print model results
print.crmlist Print model table from model list
probitCJS Perform MCMC analysis of a CJS model
proc.form Mixed effect model formula parser Parses a mixed effect model in the lme4 structure of ~fixed +(re1|g1) +...+(ren|gn)
process.ch Process release-recapture history data
process.data Process encounter history dataframe for MARK analysis
ps Mulstistate Live-Dead Paradise Shelduck Data

-- R --

reals Hidden Markov Model likelihood functions
reindex Mixed effect model contstruction
rerun_crm Automation of model runs
resight.matrix Various utility functions
R_HMMLikelihood Hidden Markov Model Functions

-- S --

scale_dm Scaling functions
scale_par Scaling functions
sealions Multivariate State example data
set.fixed Set fixed real parameter values in ddl
set.initial Set initial values
setup.model Defines model specific parameters (internal use)
setup.parameters Setup parameter structure specific to model (internal use)
setupHMM Defines model specific parameters (internal use)
setup_admb ADMB setup
setup_tmb TMB setup
set_mvms Multivariate Multistate (mvms) Specification
set_scale Scaling functions
simHMM Simulates data from Hidden Markov Model
skagit An example of the Mulstistrata (multi-state) model in which states are routes taken by migrating fish.
smsld_tmb Fitting function for Multistate CJS live-dead models with TMB
splitCH Split/collapse capture histories

-- T --

tagloss Tag loss example

-- U --

ums2_dmat HMM Observation Probability matrix functions
ums_dmat HMM Observation Probability matrix functions
unscale_par Scaling functions

-- V --

valid.parameters Determine validity of parameters for a model (internal use)