Package: binfunest 0.1.0.9000

binfunest: Estimates Parameters of Functions Driving Binomial Random Variables

Provides maximum likelihood estimates of the performance parameters that drive a binomial distribution of observed errors given signal-to-noise ratios, and takes full advantage of zero error observations. High performance communications systems typically have inherent noise sources and other performance limitations that need to be estimated. Measurements made at high signal to noise ratios typically result in zero errors due to limitation in available measurement time. Package includes theoretical performance functions for common modulation schemes (Proakis, "Digital Communications" (1995, <ISBN:0-07-051726-6>)), including polarization shifted QPSK (Agrell & Karlsson (2009, <doi:10.1109/JLT.2009.2029064>)), and utility functions to work with the performance functions.

Authors:Philip Shea [aut, cre]

binfunest_0.1.0.9000.tar.gz
binfunest_0.1.0.9000.zip(r-4.7-any)binfunest_0.1.0.9000.zip(r-4.6-any)binfunest_0.1.0.9000.zip(r-4.5-any)
binfunest_0.1.0.9000.tgz(r-4.6-any)binfunest_0.1.0.9000.tgz(r-4.5-any)
binfunest_0.1.0.9000.tar.gz(r-4.7-any)binfunest_0.1.0.9000.tar.gz(r-4.6-any)
binfunest_0.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
binfunest/json (API)

# Install 'binfunest' in R:
install.packages('binfunest', repos = c('https://philshea.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/philshea/binfunest/issues

Datasets:
  • BERDFc - An example 'BERDF' dataframe created by 'simsigs()', a function in a forthcoming package 'coherent'.

On CRAN:

Conda:

4.18 score 1 scripts 333 downloads 21 exports 1 dependencies

Last updated from:bb8ea9408c. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK108
source / vignettesOK170
linux-releaseOK109
macos-releaseOK121
macos-oldrelOK89
windows-develOK94
windows-releaseOK71
windows-oldrelOK82
wasm-releaseOK89

Exports:B2BConvertcor.mledBDBPSKdBDQPSKdBDQPSKDDdBis.wholenumbermarcumqmleB2Bmod_Invmod_InvVMPSKdBMPSKdB.8PSQPSKdBQ_Q_InvQAMdBQAMdB.16QAMdB.8.starQPSKdBundB

Dependencies:pracma

Estimating B2B Q and Offsets
Bit Error Rate Functions | Offset and a Back-to-Back SNR | Creating BER Functions with B2B & Offset | Using nls | Maximum Likelihood Estimation | Log Likelihood of observations | Using optim | Using mle | Using mleB2B | Correlation between B2B and Offset | Very High B2B Q | Adding Observations | Real Data
Rendered fromB2B_BER.Rmdusingknitr::rmarkdown

Last update: 2022-10-25
Started: 2022-08-30

Generic Simplified Log Likelihood
Rendered fromGenericQ.Rmdusingknitr::rmarkdown

Last update: 2022-10-25
Started: 2022-10-25

BER_Functions
Rendered fromBER_Functions.Rmdusingknitr::rmarkdown

Last update: 2022-10-13
Started: 2022-10-13