r-cran-amelia 1.7.1-2 source package in Ubuntu
Changelog
r-cran-amelia (1.7.1-2) unstable; urgency=low * Override lintian error due to libRcpp. -- Chris Lawrence <email address hidden> Sat, 27 Apr 2013 21:52:11 -0400
Upload details
- Uploaded by:
- Chris Lawrence
- Uploaded to:
- Sid
- Original maintainer:
- Chris Lawrence
- Architectures:
- any
- Section:
- gnu-r
- Urgency:
- Low Urgency
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Trusty | release | universe | gnu-r |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| r-cran-amelia_1.7.1-2.dsc | 1.2 KiB | fa88f9ef32d25f94de822a369368665d3904f93fa3e1aef0a37e7dfe70b9adcb |
| r-cran-amelia_1.7.1.orig.tar.gz | 1.3 MiB | e0a595428d400496fc63beebda0af6d8bb5fb24597efc1b3484e5c29c745a7af |
| r-cran-amelia_1.7.1-2.debian.tar.gz | 4.9 KiB | aa9056ab75c156b4274b889f30c56497029bd9c4a65dfd04d2acf0dfd274f299 |
Available diffs
- diff from 1.6.1-1 to 1.7.1-2 (111.5 KiB)
- diff from 1.6.1-2 to 1.7.1-2 (111.5 KiB)
No changes file available.
Binary packages built by this source
- r-cran-amelia: GNU R package supporting multiple imputation of missing data
Amelia II "multiply imputes" missing data in a single cross-section
(such as a survey), from a time series (like variables collected for
each year in a country), or from a time-series-cross-sectional data
set (such as collected by years for each of several
countries). Amelia II implements our bootstrapping-based algorithm
that gives essentially the same answers as the standard IP or EMis
approaches, is usually considerably faster than existing approaches
and can handle many more variables.
.
The program also generalizes existing approaches by allowing for
trends in time series across observations within a cross-sectional
unit, as well as priors that allow experts to incorporate beliefs
they have about the values of missing cells in their data. Amelia II
also includes useful diagnostics of the fit of multiple imputation
models. The program works from the R command line or via a graphical
user interface that does not require users to know R.
