Design | Estimators | Results | One example fit per scenario
Package: truedist 0.1.0
Paul von Hippel
truedist: Estimating the Distribution of True Effects from Noisy Estimates
Estimates the distribution of true effects (or any latent parameter) from estimates that each have a known, possibly different, standard error. The main function, em_smooth(), implements the smoothed EM (EMS) algorithm of Silverman, Jones, Wilson and Nychka (1990) <doi:10.1111/j.2517-6161.1990.tb01788.x> for a nonparametric mixing distribution on a grid. The smoothing uses one constant standard deviation, set from the typical standard error and the sample size.
Authors:
truedist_0.1.0.tar.gz
truedist_0.1.0.zip(r-4.7-any)truedist_0.1.0.zip(r-4.6-any)
truedist_0.1.0.tar.gz(r-4.7-any)truedist_0.1.0.tar.gz(r-4.6-any)
truedist_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
truedist/json (API)
| # Install 'truedist' in R: |
| install.packages('truedist', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:ee556bf97b. Checks:6 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 109 | ||
| source / vignettes | OK | 194 | ||
| linux-release | OK | 113 | ||
| windows-devel | OK | 70 | ||
| windows-release | OK | 56 | ||
| wasm-release | OK | 81 |
Exports:em_smoothnormal_fitposterior_mean
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Density of the fitted normal distribution of true effects | density.normal_fit |
| Smoothed EM estimate of the distribution of true effects | em_smooth |
| Normal model for the distribution of true effects | normal_fit |
| Plot an estimated true-effect distribution | plot.em_smooth |
| Posterior mean of each true effect | posterior_mean |
| Summary of an estimated true-effect distribution | summary.em_smooth |
| Summary of a fitted normal model | summary.normal_fit |