Nothing
#' Auxiliary functions and diagnostic plots for importance sampling
#'
#' This package contains functions computing weighted (running) summaries and diagonostic plots
#' for importance sampling problems.
#'
#' @docType package
#' @name diagis
#' @aliases diagis diagis-package
#' @importFrom coda mcmc
#' @importFrom stats ts
#' @importFrom Rcpp evalCpp
#' @useDynLib diagis
#' @examples
#' # simple importance sampling example
#' # true distribution is a standard normal:
#' p <- function(x) dnorm(x)
#' # proposal distribution is normal with sd s
#' q <- function(x, s) dnorm(x, 0, s)
#'
#' # IS weights have finite variance only if s^2 > 1/2
#' # variance is s/(2-1/s^2)^(3/2)
#'
#' #optimal case
#' set.seed(42)
#' s_opt <- sqrt(2)
#' x_opt <- rnorm(1000, sd = s_opt)
#' w_opt <- p(x_opt) / q(x_opt, s_opt)
#' weighted_mean(x_opt, w_opt)
#' weighted_var(x_opt, w_opt)
#' s_inf <- 0.25
#' x_inf <- rnorm(1000, sd = s_inf)
#' w_inf <- p(x_inf) / q(x_inf, s_inf)
#' weighted_mean(x_inf, w_inf) #!!
#' weighted_var(x_inf, w_inf) #!!
#' # diagnostic plots
#' weight_plot(w_inf)
#' weight_plot(w_opt)
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.