Package: EMMAgeo 0.9.7

EMMAgeo: End-Member Modelling of Grain-Size Data

End-member modelling analysis of grain-size data is an approach to unmix a data set's underlying distributions and their contribution to the data set. EMMAgeo provides deterministic and robust protocols for that purpose.

Authors:Michael Dietze, Elisabeth Dietze

EMMAgeo_0.9.7.tar.gz
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EMMAgeo.pdf |EMMAgeo.html✨
EMMAgeo/json (API)
NEWS

# Install 'EMMAgeo' in R:

install.packages('EMMAgeo', repos = c('https://coffeemuggler.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/coffeemuggler/emmageo/issues

Datasets:

EMpot - Example data
EMrob - Example data
X - Example data

On CRAN:

4.13 score 10 stars 27 scripts 275 downloads 1 mentions 22 exports 38 dependencies

Last updated 5 years agofrom:bfa6eb09e2. Checks:5 OK, 3 NOTE. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Feb 02 2025
R-4.5-win	NOTE	Feb 02 2025
R-4.5-mac	NOTE	Feb 02 2025
R-4.5-linux	NOTE	Feb 02 2025
R-4.4-win	OK	Feb 02 2025
R-4.4-mac	OK	Feb 02 2025
R-4.3-win	OK	Feb 02 2025
R-4.3-mac	OK	Feb 02 2025

Exports:check.data click.limits convert.units create.EM EMMA get.l get.l.opt get.limits get.q GUI interpolate.classes mix.EM model.EM residual.EM robust.EM robust.loadings robust.scores test.factors test.l test.l.max test.parameters test.robustness

Dependencies:base64enc bitops bslib cachem caTools cli commonmark crayon digest fastmap fontawesome fs glue GPArotation htmltools httpuv jquerylib jsonlite later lifecycle limSolve lpSolve magrittr MASS matrixStats memoise mime promises quadprog R6 rappdirs Rcpp rlang sass shiny sourcetools withr xtable

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
End-member modelling algorithm and supporting functions for unmixing grain-size distributions and further compositional data.	EMMAgeo-package EMMAgeo
Check correctness and consistency of input data	check.data
Define mode limits by mouse clicks.	click.limits
Convert between phi and micrometers.	convert.units
Create grain-size-distributions.	create.EM
End-member modelling analysis algorithm.	EMMA
example data	EMpot
example data	EMrob
Generate a vector of weight transformation values from l.min to l.max.	get.l
Identify optimum weight transformation value	get.l.opt
Infer lower and upper mode position limits to define robust end-members.	get.limits
Generate a parameter matrix with q.min and q.max values for robust EMMA.	get.q
Start GUI for EMMA	GUI
Interpolate data between different classes.	interpolate.classes
Function to mix sample spectres.	mix.EM
Model all possible end-member scenarios	model.EM
Calculate a residual end-member loading.	residual.EM
Extract robust end-members	robust.EM
Extract robust end-member loadings	robust.loadings
Extract robust end-member scores.	robust.scores
Calculate the initial cumulative explained variance of factors.	test.factors
Test a vector of weight transformation limits for mximum value.	test.l
Find maximum possible wight transformation value.	test.l.max
Evaluate influence of model parameters.	test.parameters
Test model robustness.	test.robustness
example data	X