R/densityGaussian.R

In luisdamiano/BayesHMM: Full Bayesian Inference for Hidden Markov Models

#' Gaussian density (univariate, continuous, unbounded space)
#'
#' @inherit Density
#' @param mu    Either a fixed value or a prior density for the location parameter.
#' @param sigma Either a fixed value or a prior density for the shape parameter.
#'
#' @family Density
#'
#' @examples
#' # With fixed values for the parameters
#' Gaussian(0, 1)
#'
#' # With priors for the parameters
#' Gaussian(
#'   mu    = Gaussian(mu = 0, sigma = 10),
#'   sigma = Cauchy(mu = 0, sigma = 10, bounds = list(0, NULL))
#' )
Gaussian <- function(mu, sigma, ordered = NULL, equal = NULL, bounds = list(NULL, NULL), trunc  = list(NULL, NULL),
                     k = NULL, r = NULL, param = NULL, ...) {
  Density("Gaussian", ordered, equal, bounds, trunc, k, r, param, mu = mu, sigma = sigma)
}

#' @keywords internal
#' @inherit freeParameters
freeParameters.Gaussian <- function(x) {
  muStr <-
    if (is.Density(x$mu)) {
      muBoundsStr <- make_bounds(x, "mu")
      sprintf(
        "real%s mu%s%s;",
        muBoundsStr, get_k(x, "mu"), get_r(x, "mu")
      )
    } else {
      ""
    }

  sigmaStr <-
    if (is.Density(x$sigma)) {
      sigmaBoundsStr <- make_bounds(x, "sigma")
      sprintf(
        "real%s sigma%s%s;",
        sigmaBoundsStr, get_k(x, "sigma"), get_r(x, "sigma")
      )
    } else {
      ""
    }

  collapse(muStr, sigmaStr)
}

#' @keywords internal
#' @inherit fixedParameters
fixedParameters.Gaussian <- function(x) {
  muStr <-
    if (is.Density(x$mu)) {
      ""
    } else {
      if (!check_scalar(x$mu)) {
        stop("If fixed, mu must be a scalar.")
      }

      sprintf(
        "real mu%s%s = %s;",
        get_k(x, "mu"), get_r(x, "mu"), x$mu
      )
    }

  sigmaStr <-
    if (is.Density(x$sigma)) {
      ""
    } else {
      if (!check_scalar(x$sigma)) {
        stop("If fixed, sigma must be a scalar.")
      }

      sprintf(
        "real sigma%s%s = %s;",
        get_k(x, "sigma"), get_r(x, "sigma"), x$sigma
      )
    }

  collapse(muStr, sigmaStr)
}

#' @keywords internal
#' @inherit generated
generated.Gaussian <- function(x) {
  sprintf(
    "if(zpred[t] == %s) ypred[t][%s] = normal_rng(mu%s%s, sigma%s%s);",
    x$k, x$r,
    get_k(x, "mu"), get_r(x, "mu"),
    get_k(x, "sigma"), get_r(x, "sigma")
  )
}

#' @keywords internal
#' @inherit getParameterNames
getParameterNames.Gaussian <- function(x) {
  return(c("mu", "sigma"))
}

#' @keywords internal
#' @inherit logLike
logLike.Gaussian <- function(x) {
  sprintf(
    "loglike[%s][t] = normal_lpdf(y[t] | mu%s%s, sigma%s%s);",
    x$k,
    get_k(x, "mu"), get_r(x, "mu"),
    get_k(x, "sigma"), get_r(x, "sigma")
  )
}

#' @keywords internal
#' @inherit prior
prior.Gaussian <- function(x) {
  truncStr <- make_trunc(x, "")
  rStr     <- make_rsubindex(x)
  sprintf(
    "%s%s%s ~ normal(%s, %s) %s;",
    x$param,
    x$k, rStr,
    x$mu, x$sigma,
    truncStr
  )
}