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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marked/json (API)
NEWS

# 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:

Conda:

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

fortrancpp

4.09 score 1 stars 1 packages 85 scripts 1.2k downloads 4 mentions 63 exports 66 dependencies

Last updated 1 years agofrom:637107c314. Checks:12 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 26 2025
R-4.5-win-x86_64OKMar 26 2025
R-4.5-mac-x86_64OKMar 26 2025
R-4.5-mac-aarch64OKMar 26 2025
R-4.5-linux-x86_64OKMar 26 2025
R-4.4-win-x86_64OKMar 26 2025
R-4.4-mac-x86_64OKMar 26 2025
R-4.4-mac-aarch64OKMar 26 2025
R-4.4-linux-x86_64OKMar 26 2025
R-4.3-win-x86_64OKMar 26 2025
R-4.3-mac-x86_64OKMar 26 2025
R-4.3-mac-aarch64OKMar 26 2025

Exports:accumulate_databackward_probcjs_deltacjs_dmatcjs_gammacjs.hessiancollapseCHcompute_matricescompute_realcreate.model.listcrmcrm.wrappercrmlist_fromfilesdmat_hsmm2hmmfix.parametersfunction.wrapperfx.aicfx.par.countglobal_decodehmmDemohsmm2hmminitiate_pijs.hessianload.modellocal_decodeloglikelihoodmake.design.datamcmc_modemerge_design.covariatesmodel.tablems_dmatms_gammams2_gammamscjsmscjs_tmbmsld_tmbmvms_design_datamvms_dmatmvmscjsmvmscjs_deltamvmscjs_tmbnaive.survivalomegap.boxplotp.meanPhi.boxplotPhi.meanprobitCJSprocess.chprocess.dataR_HMMLikelihoodrerun_crmresight.matrixset_mvmssetup_admbsetup_tmbsetup.modelsetup.parameterssimHMMsmsld_tmbsplitCHums_dmatums2_dmat

Dependencies:base64encbookdownbootbslibcachemclicodacolorspacecpp11data.tabledigestevaluateexpmfarverfastmapfontawesomefsgluehighrhtmltoolsjquerylibjsonlitekableExtraknitrlabelinglatticelifecyclelme4magrittrMASSMatrixmemoisemimeminqamunsellnlmenloptrnumDerivoptimxpracmaR2admbR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangrmarkdownrstudioapisassscalesstringistringrsvglitesystemfontstinytexTMBtruncnormvctrsviridisLitexfunxml2yaml

marked Package Vignette

Jeff L. Laake, Devin S. Johnson, and Paul B. Conn

Rendered frommarkedVignette.Rmdusingknitr::rmarkdownon Mar 26 2025.

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

Readme and manuals

Help Manual

Help pageTopics
Computes backward probabilitiesbackward_prob
Fitting function for CJS modelscjs_admb
HMM Initial state distribution functionscjs_delta mvmscjs_delta
HMM Transition matrix functionscjs_gamma ms2_gamma ms_gamma
Fitting function for CJS modelscjs_tmb
Accumulates common capture history valuescjs.accumulate
Compute variance-covariance matrix for fitted CJS modelcjs.hessian
Computes starting values for CJS p and Phi parameterscjs.initial
Likelihood function for Cormack-Jolly-Seber modelcjs.lnl
Extract coefficientscoef.crm
Compute HMM matricescompute_matrices
Compute estimates of real parameterscompute_real
Convert link values to real parametersconvert.link.to.real
Creates a design matrix for a parametercreate.dm create.dml
Creates a dataframe with all the design data for a particular parameter in a crm modelcreate.dmdf
Create parameters with fixed matrixcreate.fixed.matrix
Creates a 0/1 vector for real parameters with sin linkcreate.links
Capture-recapture model fitting functioncrm
Automation of model runscreate.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 datadipper
Create expanded state-dependent observation matrix for HMM from HSMMdmat_hsmm2hmm
Fixing real parameters in crm modelsfix.parameters
Utility extract functionsfunction.wrapper fx.aic fx.par.count
Global decoding of HMMglobal_decode
HMM computation demo functionshmmDemo
Hidden Markov Model likelihood functionshmm.lnl HMMLikelihood reals
Compute transition matrix for HMM from HSMMhsmm2hmm
Setup fixed values for pi in design datainitiate_pi
Inverse link functions (internal use)inverse.link
Fitting function for Jolly-Seber model using Schwarz-Arnason POPAN formulationjs
Accumulates common capture history valuesjs.accumulate
Compute variance-covariance matrix for fitted JS modeljs.hessian
Likelihood function for Jolly-Seber model using Schwarz-Arnason POPAN formulationjs.lnl
Local decoding of HMMlocal_decode
Create design dataframes for crmmake.design.data
Merge time (occasion) and/or group specific covariates into design datamerge.design.covariates merge_design.covariates
Mixed effect model contstructionmixed.model mixed.model.admb mixed.model.dat reindex
Fitting function for Multistate CJS modelsmscjs
Fitting function for Multistate CJS models with TMBmscjs_tmb
Fitting function for Multistate CJS live-dead models with TMBmsld_tmb
Multistrata example datamstrata
Multivariate Multistate (mvms) Design Datamvms_design_data
HMM Observation Probability matrix functionscjs_dmat ms_dmat mvms_dmat ums2_dmat ums_dmat
Fitting function for Multivariate Multistate CJS with uncertainty modelsmvmscjs
TMB version: Fitting function for Multivariate Multistate CJS with uncertainty modelsmvmscjs_tmb
Compute 1 to k-step transition proportionsomega
Mulstistate Live-Dead Paradise Shelduck DataParadise_shelduck ps
Various utility parameter summary functionsp.boxplot p.mean Phi.boxplot Phi.mean
Compute estimates of real parameterspredict.crm
Print model resultsprint.crm
Print model table from model listprint.crmlist
Perform MCMC analysis of a CJS modelprobitCJS
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 dataprocess.ch
Process encounter history dataframe for MARK analysisaccumulate_data process.data
Hidden Markov Model Functionsloglikelihood R_HMMLikelihood
Various utility functionsmcmc_mode naive.survival resight.matrix
Multivariate State example datasealions
Multivariate Multistate (mvms) Specificationset_mvms
Scaling functionsscale_dm scale_par set_scale unscale_par
Set fixed real parameter values in ddlset.fixed
Set initial valuesset.initial
ADMB setupsetup_admb
TMB setupsetup_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 ModelsimHMM
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 TMBsmsld_tmb
Split/collapse capture historiescollapseCH splitCH
Tag loss exampletagloss
Determine validity of parameters for a model (internal use)valid.parameters