Help for package thames

Type:

Package

Title:

Truncated Harmonic Mean Estimator of the Marginal Likelihood

Version:

0.1.2

Description:

Implements the truncated harmonic mean estimator (THAMES) of the reciprocal marginal likelihood using posterior samples and unnormalized log posterior values via reciprocal importance sampling. Metodiev, Perrot-Dockès, Ouadah, Irons, Latouche, & Raftery (2024). Bayesian Analysis. <doi:10.1214/24-BA1422>.

License:

GPL (≥ 3)

Encoding:

UTF-8

RoxygenNote:

7.3.2

Imports:

stats

Suggests:

knitr, markdown, rmarkdown, mvtnorm

VignetteBuilder:

knitr, rmarkdown

NeedsCompilation:

Packaged:

2025-07-14 14:21:32 UTC; njirons

Author:

Nicholas J. Irons

[aut, cre], Marie Perrot-Dockès [aut], Martin Metodiev [aut]

Maintainer:

Nicholas J. Irons <nicholasjonirons@gmail.com>

Repository:

CRAN

Date/Publication:

2025-07-14 19:10:02 UTC

Dirichlet parameter bound function

Description

Function calculating membership of a point in the interior of the simplex.

Usage

bound_dirichlet(theta)

Arguments

theta

point to check

Value

Boolean specifying whether theta lies in the interior of the simplex.

Examples

theta_ext <- runif(100)
theta_ins <- runif(100) / 100
bound_dirichlet(theta_ext)
bound_dirichlet(theta_ins)

Bounded parameter correction

Description

Computes Monte Carlo estimate of the bounded parameter correction to THAMES

Usage

bound_par_cor(theta_hat, sigma_svd, bound, radius, n_simuls = 1e+05)

Arguments

theta_hat

center of the ellipsoid A.

sigma_svd

Singular value decomposition (SVD) of the posterior covariance matrix used to define A.

bound

Function calculating membership of a point in the posterior support.

radius

positive number, radius determining the ellipsoid A

n_simuls

Integer, number of Monte Carlo simulations to use in the calculation.

Value

A number in [0,1] estimating of the proportion of the volume of A contained in the posterior support.

Examples

theta_ext <- runif(100)
theta_ins <- runif(100) / 100

Uniform sampling on/in ellipsoid

Description

Uniform sampling on an ellipsoid or in an ellipsoid. The sampling in an ellipsoid is available in arbitrary dimension. The sampling on an ellipsoid is available only in dimension 2 or 3. This code is borrowed directly from the uniformly package (https://CRAN.R-project.org/package=uniformly)

Usage

runif_on_ellipse(n, A, r)

runif_on_ellipsoid(n, A, r)

runif_in_ellipsoid(n, A, r)

Arguments

n

number of simulations

A

symmetric positive-definite matrix defining the ellipsoid (see Details), of size 2 for runif_on_ellipse and size 2 or 3 for runif_on_ellipsoid (for size 2 these are the same functions)

r

"radius" (see Details)

Details

The ellipsoid is the set of vectors x satisfying t(x) %*% A %*% x == r^2. For example, for an axis-aligned ellipse with horizontal radius a and vertical radius b, take A=1/diag(c(a^2,b^2)) and r=1.

Value

The simulations in a matrix with n rows.

Uniform sampling on/in sphere

Description

Uniform sampling on a sphere or in a sphere, in arbitrary dimension. This code is borrowed directly from the uniformly package (https://CRAN.R-project.org/package=uniformly)

Usage

runif_on_sphere(n, d, r = 1)

runif_in_sphere(n, d, r = 1)

Arguments

n

number of simulations

d

dimension of the space

r

radius of the sphere

Value

The simulations in a n times d matrix.

THAMES estimator of the (reciprocal) log marginal likelihood

Description

This function computes the THAMES estimate of the reciprocal log marginal likelihood using posterior samples and unnormalized log posterior values.

Usage

thames(
  lps = NULL,
  params,
  n_samples = NULL,
  d = NULL,
  radius = NULL,
  p = 0.025,
  q = 1 - p,
  lp_func = NULL,
  bound = NULL,
  n_simuls = 1e+05
)

Arguments

lps

vector of unnormalized log posterior values of length n_samples (sum of the log prior and the log likelihood)

params

matrix of parameter posterior samples of dimension n_samples * d

n_samples

integer, number of posterior samples

d

integer, dimension of parameter space

radius

positive number, radius to use for defining the ellipsoid A

p

percentile, used for lower bound of confidence interval

q

percentile, used for upper bound of confidence interval

lp_func

function to compute unnormalized log posterior values

bound

function calculating membership of a point in the posterior support

n_simuls

integer, number of Monte Carlo simulations to use in the bounded parameter correction calculation.

Value

Returns a named list with the following elements:

References

Metodiev M, Perrot-Dockès M, Ouadah S, Irons N. J., Latouche P., Raftery A. E. (2024) Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator. Bayesian Analysis.

Examples

mu_star = 1
n <- 50
Y = rnorm(n, mu_star, 1)
sig2 <- 1
sig2_n <- 1/(n+1/sig2)
mn <- sum(Y)/(n + 1/sig2)
params <- rnorm(20, mean=mn,  (sig2_n))
lps <-  sapply(params, function(i){
sum(dnorm(Y,i,1,log = TRUE)) + dnorm(i,0,sig2, log = TRUE)})
thames(lps, params)

Truncated normal quantiles

Description

This function computes quantiles of the truncated normal distribution for calculating THAMES confidence intervals.

Usage

trunc_quantile(p, ratio)

Arguments

p

Percentile

ratio

Ratio of standard error to point estimate (midpoint of confidence interval)

Value

Truncated normal quantile