Package: marked 1.2.8

marked: Mark-Recapture Analysis for Survival and Abundance Estimation

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.

Authors:Jeff Laake <[email protected]>, Devin Johnson <[email protected]>, Paul Conn <[email protected]>, example for simHMM from Jay Rotella

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
marked/json (API)

# Install 'marked' in R:

install.packages('marked', repos = c('https://jlaake.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

c++– GNU Standard C++ Library v3

Datasets:

dipper - Dipper capture-recapture data
mstrata - Multistrata example data
ps - Mulstistate Live-Dead Paradise Shelduck Data
sealions - Multivariate State example data
skagit - An example of the Mulstistrata (multi-state) model in which states are routes taken by migrating fish.
tagloss - Tag loss example

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

4.07 score 1 stars 1 packages 98 scripts 613 downloads 4 mentions 63 exports 65 dependencies

Last updated from:637107c314. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	176
linux-devel-x86_64	OK	185
source / vignettes	OK	238
linux-release-arm64	OK	198
linux-release-x86_64	OK	207
macos-release-arm64	OK	152
macos-release-x86_64	OK	388
macos-oldrel-arm64	OK	193
macos-oldrel-x86_64	OK	425
windows-devel	OK	239
windows-release	OK	161
windows-oldrel	OK	220
wasm-release	OK	158

Exports:accumulate_data backward_prob cjs_delta cjs_dmat cjs_gamma cjs.hessian collapseCH compute_matrices compute_real create.model.list crm crm.wrapper crmlist_fromfiles dmat_hsmm2hmm fix.parameters function.wrapper fx.aic fx.par.count global_decode hmmDemo hsmm2hmm initiate_pi js.hessian load.model local_decode loglikelihood make.design.data mcmc_mode merge_design.covariates model.table ms_dmat ms_gamma ms2_gamma mscjs mscjs_tmb msld_tmb mvms_design_data mvms_dmat mvmscjs mvmscjs_delta mvmscjs_tmb naive.survival omega p.boxplot p.mean Phi.boxplot Phi.mean probitCJS process.ch process.data R_HMMLikelihood rerun_crm resight.matrix set_mvms setup_admb setup_tmb setup.model setup.parameters simHMM smsld_tmb splitCH ums_dmat ums2_dmat

Dependencies:base64enc bookdown boot bslib cachem cli coda cpp11 data.table digest evaluate expm farver fastmap fontawesome fs glue highr htmltools jquerylib jsonlite kableExtra knitr labeling lattice lifecycle lme4 magrittr MASS Matrix memoise mime minqa nlme nloptr numDeriv optimx pracma R2admb R6 rappdirs rbibutils RColorBrewer Rcpp RcppEigen Rdpack reformulas rlang rmarkdown rstudioapi sass scales stringi stringr svglite systemfonts textshaping tinytex TMB truncnorm vctrs viridisLite xfun xml2 yaml

marked Package Vignette

Rendered frommarkedVignette.Rmdusingknitr::rmarkdown

Last update: 2019-12-09
Started: 2019-12-09

Help page	Topics
Computes backward probabilities	backward_prob
Fitting function for CJS models	cjs_admb
HMM Initial state distribution functions	cjs_delta mvmscjs_delta
HMM Transition matrix functions	cjs_gamma ms2_gamma ms_gamma
Fitting function for CJS models	cjs_tmb
Accumulates common capture history values	cjs.accumulate
Compute variance-covariance matrix for fitted CJS model	cjs.hessian
Computes starting values for CJS p and Phi parameters	cjs.initial
Likelihood function for Cormack-Jolly-Seber model	cjs.lnl
Extract coefficients	coef.crm
Compute HMM matrices	compute_matrices
Compute estimates of real parameters	compute_real
Convert link values to real parameters	convert.link.to.real
Creates a design matrix for a parameter	create.dm create.dml
Creates a dataframe with all the design data for a particular parameter in a crm model	create.dmdf
Create parameters with fixed matrix	create.fixed.matrix
Creates a 0/1 vector for real parameters with sin link	create.links
Capture-recapture model fitting function	crm
Automation of model runs	create.model.list crm.wrapper crmlist_fromfiles load.model model.table rerun_crm
Derivatives of inverse of link function (internal use)	deriv.inverse.link deriv_inverse.link
Dipper capture-recapture data	dipper
Create expanded state-dependent observation matrix for HMM from HSMM	dmat_hsmm2hmm
Fixing real parameters in crm models	fix.parameters
Utility extract functions	function.wrapper fx.aic fx.par.count
Global decoding of HMM	global_decode
HMM computation demo functions	hmmDemo
Hidden Markov Model likelihood functions	hmm.lnl HMMLikelihood reals
Compute transition matrix for HMM from HSMM	hsmm2hmm
Setup fixed values for pi in design data	initiate_pi
Inverse link functions (internal use)	inverse.link
Fitting function for Jolly-Seber model using Schwarz-Arnason POPAN formulation	js
Accumulates common capture history values	js.accumulate
Compute variance-covariance matrix for fitted JS model	js.hessian
Likelihood function for Jolly-Seber model using Schwarz-Arnason POPAN formulation	js.lnl
Local decoding of HMM	local_decode
Create design dataframes for crm	make.design.data
Merge time (occasion) and/or group specific covariates into design data	merge.design.covariates merge_design.covariates
Mixed effect model contstruction	mixed.model mixed.model.admb mixed.model.dat reindex
Fitting function for Multistate CJS models	mscjs
Fitting function for Multistate CJS models with TMB	mscjs_tmb
Fitting function for Multistate CJS live-dead models with TMB	msld_tmb
Multistrata example data	mstrata
Multivariate Multistate (mvms) Design Data	mvms_design_data
HMM Observation Probability matrix functions	cjs_dmat ms_dmat mvms_dmat ums2_dmat ums_dmat
Fitting function for Multivariate Multistate CJS with uncertainty models	mvmscjs
TMB version: Fitting function for Multivariate Multistate CJS with uncertainty models	mvmscjs_tmb
Compute 1 to k-step transition proportions	omega
Mulstistate Live-Dead Paradise Shelduck Data	Paradise_shelduck ps
Various utility parameter summary functions	p.boxplot p.mean Phi.boxplot Phi.mean
Compute estimates of real parameters	predict.crm
Print model results	print.crm
Print model table from model list	print.crmlist
Perform MCMC analysis of a CJS model	probitCJS
Mixed effect model formula parser Parses a mixed effect model in the lme4 structure of ~fixed +(re1\|g1) +...+(ren\|gn)	proc.form
Process release-recapture history data	process.ch
Process encounter history dataframe for MARK analysis	accumulate_data process.data
Hidden Markov Model Functions	loglikelihood R_HMMLikelihood
Various utility functions	mcmc_mode naive.survival resight.matrix
Multivariate State example data	sealions
Multivariate Multistate (mvms) Specification	set_mvms
Scaling functions	scale_dm scale_par set_scale unscale_par
Set fixed real parameter values in ddl	set.fixed
Set initial values	set.initial
ADMB setup	setup_admb
TMB setup	setup_tmb
Defines model specific parameters (internal use)	setup.model setupHMM
Setup parameter structure specific to model (internal use)	setup.parameters
Simulates data from Hidden Markov Model	simHMM
An example of the Mulstistrata (multi-state) model in which states are routes taken by migrating fish.	skagit
Fitting function for Multistate CJS live-dead models with TMB	smsld_tmb
Split/collapse capture histories	collapseCH splitCH
Tag loss example	tagloss
Determine validity of parameters for a model (internal use)	valid.parameters

Package: marked 1.2.8

marked: Mark-Recapture Analysis for Survival and Abundance Estimation

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