R: Mark-Recapture Analysis for Survival and Abundance Estimation

# Mark-Recapture Analysis for Survival and Abundance Estimation

## Documentation for package ‘marked’ version 1.2.8

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Code demos. Use demo() to run them.
- Package NEWS

## Help Pages

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)