A B C D F G H I J L M N O P R S T U V
| accumulate_data | Process encounter history dataframe for MARK analysis |
| backward_prob | Computes backward probabilities |
| 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 |
| 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 |
| fix.parameters | Fixing real parameters in crm models |
| function.wrapper | Utility extract functions |
| fx.aic | Utility extract functions |
| fx.par.count | Utility extract functions |
| global_decode | Global decoding of HMM |
| 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 |
| initiate_pi | Setup fixed values for pi in design data |
| inverse.link | Inverse link functions (internal use) |
| 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 |
| load.model | Automation of model runs |
| local_decode | Local decoding of HMM |
| loglikelihood | Hidden Markov Model Functions |
| 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 |
| naive.survival | Various utility functions |
| omega | Compute 1 to k-step transition proportions |
| 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 |
| 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 |
| 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 |
| set.scale | Scaling functions |
| 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 |
| 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. |
| splitCH | Split/collapse capture histories |
| tagloss | Tag loss example |
| ums2_dmat | HMM Observation Probability matrix functions |
| ums_dmat | HMM Observation Probability matrix functions |
| unscale.par | Scaling functions |
| valid.parameters | Determine validity of parameters for a model (internal use) |