Author: Jan-Ole Fischer

# 1 Introduction to LaMa

#### Jan-Ole Fischer

The `R` package `LaMa` provides convenient functions for fitting a variety of latent Markov models, including hidden Markov models (HMMs), hidden semi-Markov models (HSMMs), state space models (SSMs) and continuous-time variants via direct numerical maximum likelihood estimation. The core idea underlying this package is given in Mews et al. (2025), namely for all these models the likelihood function has the same structure, and hence unification of inference procedures is attractive.

Model specification in `LaMa` amounts to defining custom likelihood function using provided building-blocks – similar to stacking Legos. This approach is different (and arguably more difficult) than model specification in packages like `moveHMM`, `momentuHMM`, or `hmmTMB`. The modular approach, however, has the advantage of being more flexible. Specifying simple models is still very easy and convenient while model complexity can be increased to an arbitrary level and arbitrarily fine control is available for advanced users.

The main building-block functions to define the likelihood are `forward`, `tpm` and `stationary` and we showcase the simplest versions in the following introductory example. Downstream functions for state decoding and residual calculation are also available.

## Introductory example: Homogeneous HMM

In this vignette, we start from the most basic hidden Markov model (HMM) we can think of. Such a basic \(N\)-state HMM is a doubly stochastic process in discrete time. Observations are generated by one of \(N\) possible distributions \(f_j(x_t)\), \(j = 1, \dots N\) with an unobserved \(N\)-state Markov chain selecting which distribution is active at any given time point. Hence, HMMs are temporally-dependent mixture models. For a more exhaustive description of such models see Zucchini et al. (2016). They are very popular across a wide range of empirical disciplines including ecology, sports and finance, where time-series data with underlying sequential dependencies need to be analysed. The above definition already hints at the two main model assumptions:

1. \(f(s_t \mid s_{t-1}, s_{t-2}, \dots, s_1) = f(s_t \mid s_{t-1})\) (Markov assumption)
2. \(f(x_t \mid x_1, \dots, x_{t-1}, x_{t-1}, x_T, s_1, \dots, s_T) = f(x_t \mid s_t)\) (conditional independence assumption).

The hidden state process is described by an \(N\)-state Markov chain which is fully characterised by its initial distribution \[\delta^{(1)} = (\Pr(S_1 = 1), \dots, \Pr(S_1 = N))\] and the one-step transition probabilities \[\gamma_{ij} = \Pr(S_t = j \mid S_{t-1} = i), \quad i,j = 1, \dotsc, N.\] These are typically summarised in a transition probability matrix (t.p.m.) \[\Gamma = (\gamma_{ij})_{i,j = 1, \dots, N},\] where row \(i\) is the conditional one-step-ahead distribution of the state process given that the current state is \(i\). For example a t.p.m. for a two state Markov chain could be

    (Gamma = matrix(c(0.9, 0.1,
                      0.2, 0.8), nrow = 2, byrow = TRUE))
    #>      [,1] [,2]
    #> [1,]  0.9  0.1
    #> [2,]  0.2  0.8

We often assume stationarity of the underlying Markov chain because well-behaved Markov chains converge to a unique stationary distribution. When we e.g. observe an animal and model its behavioral states by a Markov chain, it is reasonable to assume that the chain has been running for a long time prior to our observation and thus already converged to its stationary distribution. This distribution (which we call \(\delta\)) can be computed by solving the system of equations \[ \delta \Gamma = \delta, \quad \text{s.t.} \; \sum_{j=1}^N \delta_j = 1, \] which is implemented in the function `stationary()`. For stationary HMMs, we then replace the initial distribution \(\delta^{(1)}\) by this stationary distribution. We can easily compute the stationary distribution associated with the above t.p.m. using

    (delta = stationary(Gamma))
    #>        S1        S2
    #> 0.6666667 0.3333333

This stationary distribution can be interpreted as the log-run-time proportion of time spent in each state. For the conditional distributions of the observations \(f_j(x_t)\), a typical choice would be some kind of parametric family like normal or gamma distributions with state-specific means and standard deviations.

### Generating data from a 2-state HMM

To get an intuition for this model, we simulating some data from a simple 2-state HMM with Gaussian state-dependent distributions. Here we can again use `stationary()` to compute the stationary distribution.

    # parameters
    mu = c(0, 6)    # state-dependent means
    sigma = c(2, 4) # state-dependent standard deviations
    Gamma = matrix(c(0.95, 0.05, # transition probability matrix
                     0.15, 0.85),
                   nrow = 2, byrow = TRUE)
    delta = stationary(Gamma) # stationary distribution

    # simulation
    n = 1000
    set.seed(123)
    s = rep(NA, n)
    s[1] = sample(1:2, 1, prob = delta) # sampling first state from delta
    for(t in 2:n){
      # drawing the next state conditional on the last one
      s[t] = sample(1:2, 1, prob = Gamma[s[t-1],])
    }
    # drawing the observation conditional on the states
    x = rnorm(n, mu[s], sigma[s]) # independent given the state sequence

    color = c("orange", "deepskyblue")
    plot(x[1:200], bty = "n", pch = 20, ylab = "x",
         col = color[s[1:200]])

### Inference by direct numerical maximum likelihood estimation

Inference for HMMs is more difficult compared to e.g. regression modelling as the observations are not independent. We want to estimate model parameters via maximum likelihood estimation, due to the nice properties possessed by the maximum likelihood estimator. However, computing the HMM likelihood for observed data points \(x_1, \dots, x_T\) is a non-trivial task as we do not observe the underlying states. We need to sum out all possible state sequences which would be infeasible for general state processes. We can, however, exploit the Markov property and calculate the likelihood recursively as a matrix product using the so-called forward algorithm. In closed form, the HMM likelihood then becomes

\[ L(\theta) = \delta^{(1)} P(x_1) \Gamma P(x_2) \Gamma \dots \Gamma P(x_T) 1^t, \] where \(\delta^{(1)}\) and \(\Gamma\) are as defined above, \(P(x_t)\) is a diagonal matrix with state-dependent densities or probability mass functions \(f_j(x_t) = f(x_t \mid S_t = j)\) on its diagonal and \(1\) is a row vector of ones with length \(N\). All model parameters are here summarised in the vector \(\theta\). Being able to evaluate the likelihood function, it can be numerically maximised by e.g. `nlm()`.

The algorithm explained above suffers from numerical underflow and for \(T\) only moderately large the likelihood is rounded to zero. Thus, one can use a scaling strategy, detailed by Zucchini et al. (2016), to avoid this and calculate the log-likelihood recursively. This version of the forward algorithm is implemented in `LaMa` and written in `C++` for speed.

Additionally, for HMMs we often need to constrain the domains of several of the model parameters in \(\theta\) (i.e. positive standard deviations or a transition probability matrix with elements between 0 and 1 and rows that sum to one). One could now resort to constrained numerical optimisation but in practice the better option is to maximise the likelihood w.r.t. a transformed version (to the real number line) of the model parameters by using suitable invertible and differentiable link functions (denoted by `par` in the code). For example we use the log-link for parameters that need to be strictly positive and the multinomial logistic link (softmax) for the transition probability matrix. While the former can easily be coded by hand, the latter is implemented in the functions of the `tpm` family for convenience and computational speed.

For efficiency, it is also advisable to evaluate the state-dependent densities (or probability mass functions) vectorised outside the recursive forward algorithm. This results in a matrix containing the state-dependent likelihoods for each data point (rows), conditional on each state (columns), which, throughout the package, we call the `allprobs` matrix.

In this example, within the negative log-likelihood function we

1. build the transition probability matrix using the `tpm()` function,
2. compute the stationary distribution of the Markov chain using `stationary()`,
3. build the `allprobs` matrix, and
4. calculate the log-likelihood using `forward()` in the last line.

We return \(-1\) times the log-likelihood such that it can be numerically minimised.

    nll = function(par, x){
      # 1) Transition probability matrix
      Gamma = tpm(par[1:2]) # multinomial logistic link (softmax)
      # 2) Stationary distribution for this matrix
      delta = stationary(Gamma) # will be used as initial
      # Parameter transformations for unconstrained optimisation
      mu = par[3:4] # no transformation needed
      sigma = exp(par[5:6]) # strictly positive
      # 3) Calculating state-dependent densities outside the forward algorithm
      allprobs = matrix(1, length(x), 2)
      for(j in 1:2) allprobs[,j] = dnorm(x, mu[j], sigma[j])
      # 4) Run the forward algorithm
      -forward(delta, Gamma, allprobs)
    }

We can now fit the model to data by defining a vector of initial parameter values on the transformed scale and calling `nlm()` to numerically maximise the likelihood. The initial parameters are not chosen too well here, but the estimation procedure still converges to the correct values. In practice, it is advisable to try several different sets of initial parameters to ensure convergence to the global maximum.

The `tpm()` function uses the multinomial logistic (softmax) link to map unconstrained real-valued parameters to valid transition probabilities. To initialise near a specific off-diagonal probability \(p\), we therefore supply \(\text{logit}(p) = \log(p / (1-p))\), i.e. `qlogis(p)`, as the starting value. Here we start near 0.05 for both off-diagonal entries — not too far from the truth but intentionally imperfect:

    par = c(logitGamma = qlogis(c(0.05, 0.05)),
            mu = c(1,4),
            logsigma = c(log(1),log(3)))
    # initial transformed parameters: not chosen too well
    system.time(
      opt <- nlm(nll, par, x = x)
    )
    #>    user  system elapsed
    #>   0.046   0.000   0.046

We see that implementation of the forward algorithm in C++ leads to really fast estimation speeds.

### Visualising results

After model estimation, we need to retransform the unconstrained parameters according to the code inside the likelihood:

    estimate = opt$estimate
    # transform parameters to natural scale
    Gamma = tpm(estimate[1:2])
    delta = stationary(Gamma) # stationary HMM
    mu = estimate[3:4]
    sigma = exp(estimate[5:6])

    hist(x, prob = TRUE, bor = "white", breaks = 40, main = "")
    curve(delta[1]*dnorm(x, mu[1], sigma[1]), add = TRUE, lwd = 2, col = "orange", n=500)
    curve(delta[2]*dnorm(x, mu[2], sigma[2]), add = TRUE, lwd = 2, col = "deepskyblue", n=500)
    curve(delta[1]*dnorm(x, mu[1], sigma[1])+delta[2]*dnorm(x, mu[2], sigma[2]),
          add = TRUE, lwd = 2, lty = "dashed", n=500)
    legend("topright", col = c(color, "black"), lwd = 2, bty = "n",
           lty = c(1,1,2), legend = c("state 1", "state 2", "marginal"))

We can also decode the most probable state sequence with the `viterbi()` function, when first computing the `allprobs` matrix:

    allprobs = matrix(1, length(x), 2)
    for(j in 1:2) allprobs[,j] = dnorm(x, mu[j], sigma[j])

    states = viterbi(delta, Gamma, allprobs)

    plot(x, pch = 20, bty = "n", col = color[states])
    legend("topright", pch = 20, legend = c("state 1", "state 2"),
           col = color, box.lwd = 0)

Lastly, we can do some model-checking using pseudo-residuals. First, we need to compute the local state probabilities of our observations:

    probs = stateprobs(delta, Gamma, allprobs)

Then, we can pass the observations, the state probabilities, the parametric family and the estimated parameters to the `pseudo_res()` function to get pseudo-residuals for model validation. These should be standard normally distributed if the model is correct.

    pres = pseudo_res(x, # observations
                      "norm", # parametric distribution to use
                      list(mean = mu, sd = sigma), # parameters for that distribution
                      probs) # local state probabilities

    # use the plotting method for LaMaResiduals
    plot(pres, hist = TRUE)

In this case, our model looks really good – which it should because it is correctly specified.

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## References

Mews, Sina, Jan-Ole Koslik, and Roland Langrock. 2025. “How to Build Your Latent Markov Model: The Role of Time and Space.” Statistical Modelling 0 (0). https://doi.org/10.1177/1471082X251355681.
Zucchini, Walter, Iain L. MacDonald, and Roland Langrock. 2016. Hidden Markov Models for Time Series: An Introduction Using R. Boca Raton: Chapman & Hall/CRC.