Fit Hidden Markov Models using Template Model Builder
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Documentation for package ‘hmmTMB’ version 2.0.0
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| as_character_formula | Read formula with as.character without splitting |
| as_sparse | Transforms matrix to dgTMatrix |
| bdiag_check | Create block diagonal matrix (safe version) |
| check_contiguous | Check values in vector are contiguous |
| check_smooths | Check that only supported smooths are used |
| cov_grid | Grid of covariates |
| cov_grid_2d | Grid of covariate values over two covariates |
| cov_names_in_data | Covariate names that are columns of the data |
| Dist | R6 class for probability distribution |
| dvm | Density function of von Mises distribution |
| dwrpcauchy | Density function of wrapped Cauchy distribution |
| find_re | Find s(, bs = "re") terms in formula |
| Forecast | R6 Class for Forecast in Hidden Markov Models |
| gam_shell | Shell gam object for building prediction matrices |
| gdeterminant | Generalized matrix determinant |
| HMM | R6 class for hidden Markov model |
| hmmTMB_cols | hmmTMB colour palette |
| invmlogit | Multivarite inverse logit function |
| is_whole_number | Check if number of whole number |
| logLik.HMM | logLik function for SDE objects |
| logsumexp | Log of sum of exponentials |
| make_cov | Make covariance matrix from standard deviations and correlations |
| make_formulas | Process formulas and store in nested list |
| make_matrices | Create model matrices |
| MarkovChain | R6 class for HMM hidden process model |
| mlogit | Multivariate logit function |
| mvnorm_invlink | Multivariate Normal inverse link function |
| mvnorm_link | Multivariate Normal link function |
| na_fill | Fill in NAs |
| Observation | R6 class for HMM observation model |
| prec_to_cov | Get covariance matrix from precision matrix |
| Predict.matrix.spde.smooth | Matern SPDE smooth |
| quad_pos_solve | Solve for positive root of quadratic ax^2 + bx + c = 0 when it exists |
| rmvn_prec | Sample from a multivariate normal given its precision matrix |
| rvm | Sample from von Mises distribution |
| rwrpcauchy | Sample from wrapped Cauchy distribution |
| smooth.construct.spde.smooth.spec | Matern SPDE smooth |
| spde_extent | A length scale for the meshed domain, for starting values |
| spde_loc | Observation locations for an SPDE smooth |
| spde_mesh_1d | Default mesh for a one-dimensional SPDE smooth |
| strip_comments | Strip comments marked with a hash from a character vector |
| update.HMM | Update a model to a new model by changing one formula |