Package: lognorm 0.1.9

lognorm: Functions for the Lognormal Distribution

The lognormal distribution (Limpert et al. (2001) <doi:10.1641/0006-3568(2001)051%5B0341:lndats%5D2.0.co;2>) can characterize uncertainty that is bounded by zero. This package provides estimation of distribution parameters, computation of moments and other basic statistics, and an approximation of the distribution of the sum of several correlated lognormally distributed variables (Lo 2013 <doi:10.12988/ams.2013.39511>) and the approximation of the difference of two correlated lognormally distributed variables (Lo 2012 <doi:10.1155/2012/838397>).

Authors:Thomas Wutzler

lognorm_0.1.9.tar.gz
lognorm_0.1.9.zip(r-4.7)lognorm_0.1.9.zip(r-4.6)lognorm_0.1.9.zip(r-4.5)
lognorm_0.1.9.tgz(r-4.6-any)lognorm_0.1.9.tgz(r-4.5-any)
lognorm_0.1.9.tar.gz(r-4.7-any)lognorm_0.1.9.tar.gz(r-4.6-any)
lognorm_0.1.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
lognorm/json (API)

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

Bug tracker:https://github.com/bgctw/lognorm/issues

On CRAN:

Conda:

5.82 score 6 stars 74 scripts 387 downloads 25 exports 2 dependencies

Last updated from:9cc622e5e3. Checks:8 OK, 1 ERROR. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK142
source / vignettesOK188
linux-release-x86_64OK142
macos-release-arm64OK107
macos-oldrel-arm64OK81
windows-develOK89
windows-releaseOK91
windows-oldrelERROR96
wasm-releaseOK105

Exports:computeEffectiveAutoCorrcomputeEffectiveNumObsestimateDiffLognormalestimateParmsLognormFromSampleestimateStdErrParmsestimateSumLognormalestimateSumLognormalSampleestimateSumLognormalSampleExpScalegetCorrMatFromAcfgetLognormMediangetLognormModegetLognormMomentsgetParmsLognormForExpvalgetParmsLognormForLowerAndUppergetParmsLognormForLowerAndUpperLoggetParmsLognormForMeanAndUppergetParmsLognormForMedianAndUppergetParmsLognormForModeAndUppergetParmsLognormForMomentspDiffLognormalSamplescaleLogToOrigscaleOrigToLogseCorsetMatrixOffDiagonalsvarCor

Dependencies:latticeMatrix

Approximating the difference of two lognormal random variables
Approximating the difference of lognormal random variables | Two uncorrelated random variables | Test if difference is significantly different from zero | Two positively correlated variables | Subtracting a variable with larger variance
Rendered fromlognormalDiff.Rmdusingknitr::rmarkdown

Last update: 2021-03-10
Started: 2020-06-30

Approximating the sum of lognormal random variables
Two uncorrelated random variables | Many correlated variables | Generating observations and log-normally distributed random errors | Estimating the correlation matrix and effective number of parameters | Computing the mean and its standard deviation
Rendered fromlognormalSum.Rmdusingknitr::rmarkdown

Last update: 2021-03-10
Started: 2018-08-07

Using the lognorm package
Distribution | Shape by multiplicative standard deviation | Density, distribution function, quantile function and random generation | Expected value, Variance, Mode, and Median | Parameter Estimation from moments
Rendered fromlognorm.Rmdusingknitr::rmarkdown

Last update: 2021-03-10
Started: 2018-08-07

Readme and manuals

Help Manual

Help pageTopics
Estimate vector of effective components of the autocorrelationcomputeEffectiveAutoCorr
Compute the effective number of observations taking into account autocorrelationcomputeEffectiveNumObs
Inference on the difference of two lognormalsestimateDiffLognormal pDiffLognormalSample
Estimate lognormal distribution parameters from a sampleestimateParmsLognormFromSample estimateStdErrParms
Estimate the parameters of the lognormal approximation to the sumestimateSumLognormal estimateSumLognormalSample estimateSumLognormalSampleExpScale
Construct the full correlation matrix from autocorrelation components.getCorrMatFromAcf
Compute summary statistics of a log-normal distributiongetLognormMedian getLognormMode getLognormMoments
Calculate mu and sigma of lognormal from summary statistics.getParmsLognormForExpval getParmsLognormForLowerAndUpper getParmsLognormForLowerAndUpperLog getParmsLognormForMeanAndUpper getParmsLognormForMedianAndUpper getParmsLognormForModeAndUpper getParmsLognormForMoments
Scale standard deviation between log and original scale.scaleLogToOrig scaleOrigToLog
Compute the standard error accounting for empirical autocorrelationsseCor
set off-diagonal values of a matrixsetMatrixOffDiagonals
Compute the unbiased variance accounting for empirical autocorrelationsvarCor