Fast Numerical Maximum Likelihood Estimation for Latent Markov Models
Build the design and penalty matrices for smooth density estimation
Calculate the index of the first observation of each track based on an...
Reparametrised multivariate Gaussian distribution
General [forward algorithm](https://www.taylorfrancis.com/books/mono/1...
[Forward algorithm](https://www.taylorfrancis.com/books/mono/10.1201/b...
[Forward algorithm](https://www.taylorfrancis.com/books/mono/10.1201/b...
[Forward algorithm](https://www.taylorfrancis.com/books/mono/10.1201/b...
[Forward algorithm](https://www.taylorfrancis.com/books/mono/10.1201/b...
[Forward algorithm](https://www.taylorfrancis.com/books/mono/10.1201/b...
[Forward algorithm](https://www.taylorfrancis.com/books/mono/10.1201/b...
[Forward algorithm](https://www.taylorfrancis.com/books/mono/10.1201/b...
Reparametrised gamma distribution
Build the generator matrix of a continuous-time Markov chain
LaMa: Fast Numerical Maximum Likelihood Estimation for Latent Markov M...
Build a standardised P-Spline design matrix and the associated P-Splin...
Build the design matrix and the penalty matrix for models involving pe...
Computes penalty based on quadratic form
Build the prediction design matrix based on new data and model_matrice...
Calculate pseudo-residuals for discrete-valued observations
Calculate pseudo-residuals
Quasi restricted maximum likelihood (qREML) algorithm for models with ...
Monte Carlo version of sdreport
Calculate conditional local state probabilities for inhomogeneous HMMs
Calculate conditional local state probabilities for periodically inhom...
Calculate conditional local state probabilities for homogeneous HMMs
Compute the stationary distribution of a continuous-time Markov chain
Sparse version of stationary_p
Compute the periodically stationary distribution of a periodically inh...
Sparse version of stationary
Compute the stationary distribution of a homogeneous Markov chain
Calculate continuous time transition probabilities
Build all embedded transition probability matrices of an inhomogeneous...
Build the embedded transition probability matrix of an HSMM from uncon...
Build all transition probability matrices of an inhomogeneous HMM
Builds the transition probability matrix of an HSMM-approximating HMM
Build the transition probability matrix of an HSMM-approximating HMM
Builds all transition probability matrices of an inhomogeneous-HSMM-ap...
Build all transition probability matrices of a periodically inhomogene...
Builds all transition probability matrices of an periodic-HSMM-approxi...
Build all transition probability matrices of an periodic-HSMM-approxim...
Compute the transition probability matrix of a thinned periodically in...
Build the transition probability matrix from unconstrained parameter v...
Compute the design matrix for a trigonometric basis expansion
Viterbi algorithm for state decoding in inhomogeneous HMMs
Viterbi algorithm for state decoding in periodically inhomogeneous HMM...
Viterbi algorithm for state decoding in homogeneous HMMs
von Mises distribution
A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.