Description Usage Arguments Details Value

Constructs an object of class `llg_ssm`

by defining the corresponding terms
of the observation and state equation:

1 2 3 4 |

`y` |
Observations as multivariate time series (or matrix) of length |

`Z, H, T, R, a1, P1, obs_intercept, state_intercept` |
An external pointers for the C++ functions which define the corresponding model functions. |

`theta` |
Parameter vector passed to all model functions. |

`known_params` |
Vector of known parameters passed to all model functions. |

`known_tv_params` |
Matrix of known parameters passed to all model functions. |

`n_states` |
Number of states in the model. |

`n_etas` |
Dimension of the noise term of the transition equation. |

`log_prior_pdf` |
An external pointer for the C++ function which computes the log-prior density given theta. |

`time_varying` |
Optional logical vector of length 6, denoting whether the values of Z, H, T, R, D and C can vary with respect to time variable. If used, can speed up some computations. |

`state_names` |
Names for the states. |

*y_t = D(t,θ) + Z(t,θ) α_t + H(t, θ) ε_t, (\textrm{observation equation})*

*α_{t+1} = C(t,θ) + T(t, θ) α_t + R(t, θ)η_t, (\textrm{transition equation})*

where *ε_t \sim N(0, I_p)*, *η_t \sim N(0, I_m)* and
*α_1 \sim N(a_1, P_1)* independently of each other.

Compared to other models, these general models need a bit more effort from the user, as you must provide the several small C++ snippets which define the model structure. See examples in the vignette.

Object of class `llg_ssm`

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