Package: NAP
Type: Package
Title: Non-Local Alternative Priors in Psychology
Version: 1.1
Date: 2022-1-6
Authors@R: c(person("Sandipan", "Pramanik", email = "sandy@stat.tamu.edu",
role = c("aut", "cre")),
person("Valen E.", "Johnson", role = "aut"))
Author: Sandipan Pramanik [aut, cre],
Valen E. Johnson [aut]
Maintainer: Sandipan Pramanik <sandy@stat.tamu.edu>
Description: Conducts Bayesian Hypothesis tests of a point null hypothesis against a two-sided alternative
using Non-local Alternative Prior (NAP) for one- and two-sample z- and t-tests
(Pramanik and Johnson, 2022). Under the alternative, the NAP is assumed on the standardized
effects size in one-sample tests and on their differences in two-sample tests. The package
considers two types of NAP densities: (1) the normal moment prior, and (2) the composite alternative.
In fixed design tests, the functions calculate the Bayes factors and the expected weight of evidence
for varied effect size and sample size. The package also provides a sequential testing framework using the
Sequential Bayes Factor (SBF) design. The functions calculate the operating characteristics (OC)
and the average sample number (ASN), and also conducts sequential tests for a sequentially observed data.
Imports: foreach, stats, utils, parallel, doParallel, graphics
License: GPL (>= 2)
NeedsCompilation: no
Packaged: 2022-01-06 04:40:59 UTC; sandipanpramanik
Repository: CRAN
Date/Publication: 2022-01-06 12:30:02 UTC

Title: Performs the processing and normalisation of Nanostring miRNA
Description: Enables the processing and normalisation of the standard mRNA data output from the Nanostring nCounter software

Title: Performs the Processing and Normalisation of Nanostring miRNA
Depends: R (>= 3.0.1), plyr (>= 1.8), NanoStringNorm (>= 1.1)
Description: Enables the processing and normalisation of the standard mRNA data output from the Nanostring nCounter software.

Title: Maximum Likelihood method in estimating effective population
Author: Tin-Yu Hui @ Imperial College London
Description: Estimate the effective population size of a closed population using genetic data collected from two or more data points.