temporal_tvc: Time-varying coefficient structure

In tulpa: Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

Time-varying coefficient structure

Description

Specify a time-varying coefficient (TVC): one or more fixed-effect coefficients are allowed to evolve over time, with the evolution governed by a temporal prior (rw1, rw2, ar1 or gp).

rw1, rw2 and ar1 read the time index as a position on a grid, so consecutive instants are one step apart whatever the data says. gp is the continuous-time structure: the coefficient is a Gaussian process over the distinct time VALUES, which is what irregular spacing needs. It is distinct from temporal_gp(), a GP over time entering the linear predictor additively (⁠eta_i += f(t_i)⁠); here the GP IS a coefficient (⁠eta_i += x_i w(t_i)⁠).

Usage

temporal_tvc(
  time_var,
  terms = 1,
  structure = c("rw1", "rw2", "ar1", "gp"),
  cov = c("exponential", "matern", "gaussian", "periodic"),
  nu = 1.5,
  period = NULL,
  group_var = NULL,
  shared = NULL,
  sigma_prior_U = 1,
  sigma_prior_alpha = 0.01,
  scale_coords = TRUE
)

## S3 method for class 'tulpa_tvc'
print(x, ...)

Arguments

time_var	Single character string naming the time variable in the data. structure = "gp" needs it numeric: a factor states an ordering with no spacing for the kernel to measure.
terms	Which coefficients vary over time. A formula, an integer vector of design-matrix column indices, or a character vector of term names. Default 1 (the intercept).
structure	Temporal prior governing how the coefficients evolve. One of "rw1", "rw2", "ar1" or "gp".
cov, nu, period	Covariance kernel for structure = "gp", ignored otherwise. cov is one of "exponential" (the default), "matern", "gaussian" or "periodic"; nu is the Matern smoothness, closed-form at 0.5, 1.5 and 2.5 only (0.5 IS the exponential kernel); period is the periodic kernel's period. Exponential and Matern nu = 0.5 evaluate in O(T) through the Ornstein-Uhlenbeck factorization; the rest take a dense ⁠T x T⁠ Cholesky per coefficient per gradient evaluation.
group_var	Optional character string naming a grouping variable for group-specific time-varying coefficients.
shared	Whether the effect is shared across processes in a multi-process model. NULL (default) shares it; FALSE fits process-specific effects and emits a warning.
sigma_prior_U, sigma_prior_alpha	Penalized-complexity prior on each varying coefficient's marginal standard deviation, calibrated so that P(sigma > sigma_prior_U) = sigma_prior_alpha. Defaults to P(sigma > 1) = 0.01. sigma_prior_U must be positive and sigma_prior_alpha must lie in ⁠(0, 1)⁠. Read on every structure: it is the same anchor pair whether the field samples a log-precision (rw1 / rw2 / ar1) or a log-variance (gp).
scale_coords	Logical, structure = "gp" only: standardize the time values before fitting (default TRUE), which puts the lengthscale on the same universal support temporal_gp() uses. period is stated in the raw time units and makes the same trip.
x	A tulpa_tvc specification.
...	Ignored.

Value

A tulpa_tvc object.

See Also

temporal_rw1(), temporal_rw2(), temporal_ar1() for the underlying temporal priors; temporal_gp() for a GP over time that is not a varying coefficient.

Examples

# Intercept that drifts as a first-order random walk over year
temporal_tvc("year", structure = "rw1")

# A slope evolving as a continuous-time GP over irregularly-spaced visits
temporal_tvc("day", terms = ~ x - 1, structure = "gp", cov = "matern")

tulpa documentation built on Oct. 19, 2026, 5:09 p.m.