Package: lmap 0.1.2

lmap: Logistic Mapping

Set of tools for mapping of categorical response variables based on principal component analysis (pca) and multidimensional unfolding (mdu).

Authors:Mark de Rooij [aut, cre, cph], Frank Busing [aut, cph], Juan Claramunt Gonzalez [aut]

lmap_0.1.2.tar.gz
lmap_0.1.2.zip(r-4.5)lmap_0.1.2.zip(r-4.4)lmap_0.1.2.zip(r-4.3)
lmap_0.1.2.tgz(r-4.4-x86_64)lmap_0.1.2.tgz(r-4.4-arm64)lmap_0.1.2.tgz(r-4.3-x86_64)lmap_0.1.2.tgz(r-4.3-arm64)
lmap_0.1.2.tar.gz(r-4.5-noble)lmap_0.1.2.tar.gz(r-4.4-noble)
lmap_0.1.2.tgz(r-4.4-emscripten)lmap_0.1.2.tgz(r-4.3-emscripten)
lmap.pdf |lmap.html✨
lmap/json (API)

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

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

9 exports 0.09 score 142 dependencies 3 scripts 226 downloads

Last updated 6 months agofrom:1411a85d03. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 03 2024
R-4.5-win-x86_64OKSep 03 2024
R-4.5-linux-x86_64OKSep 03 2024
R-4.4-win-x86_64OKSep 03 2024
R-4.4-mac-x86_64OKSep 03 2024
R-4.4-mac-aarch64OKSep 03 2024
R-4.3-win-x86_64OKSep 03 2024
R-4.3-mac-x86_64OKSep 03 2024
R-4.3-mac-aarch64OKSep 03 2024

Exports:clmduclpcaesmfastmbufastmrulmdulpcamrutwomodedistance

Dependencies:abindbackportsbase64encbitbit64bootbroombslibcachemcandisccarcarDatacheckmateclassclicliprclustercodetoolscolorspacecowplotcpp11crayondata.tableDerivdigestdoBydoParalleldplyre1071ellipseevaluatefansifarverfastmapfmdufontawesomeforcatsforeachforeignFormulafsgdatagenericsggforceggplot2ggrepelglmnetgluegridExtragtablegtoolshavenheplotshighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjomojquerylibjsonliteknitrlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmemoisemgcvmicemicrobenchmarkmimeminqamitmlmodelrmunsellnlmenloptrnnetnnlsnumDerivordinalpanpbkrtestpillarpkgconfigplotrixpolyclippolynomprettyunitsprogressproxypurrrquantregR6rappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppGSLRcppParallelRcppZigguratreadrRfastrglrlangrmarkdownrpartrstudioapisassscalesshapesmacofSparseMstringistringrsurvivalsystemfontstibbletidyrtidyselecttinytextweenrtzdbucminfutf8vctrsviridisviridisLitevroomweightswithrwordcloudxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Cumulative Logistic (Restricted) MDUclmdu
Cumulative Logistic (Restrcited) PCAclpca
Dummy data for clmdu exampledataExample_clmdu
Dummy data for clpca exampledataExample_clpca
Dummy data for lmdu exampledataExample_lmdu
Dummy data for lpca exampledataExample_lpca
Dummy data for mru exampledataExample_mru
Extended Stereotype Modelesm
Fast version of mbu. It runs mbu without input checks.fastmbu
Fast version of mru. It runs mru without input checks.fastmru
Logistic (Restricted) MDUlmdu
Logistic (Restricted) PCAlpca
Multinomial Restricted MDUmru
Plots a Cumulative Logistic MDU modelplot.clmdu
Plots a Cumulative Logistic PCA modelplot.clpca
Plots a Logistic MDU modelplot.lmdu
Plots a Logistic PCA Modelplot.lpca
Plots a Multinomial Restricted MDU modelplot.mru
The function predict.clmdu makes predictions for a test/validation set based on a fitted cl restricted multidimensional unfolding model (clmdu with X)predict.clmdu
The function predict.clpca makes predictions for a test/validation set based on a fitted clrrr model (clpca with X)predict.clpca
The function predict.lmdu makes predictions for a test/validation set based on a fitted lrmdu model (lmdu with X)predict.lmdu
The function predict.lpca makes predictions for a test/validation set based on a fitted lrrr model (lpca with X)predict.lpca
The function predict.mru makes predictions for a test/validation set based on a fitted mru modelpredict.mru
Summarizing Cumulative Logistic MDU models The function summary.lmdu gives a summary from an object from clmdu()summary.clmdu
Summarizing Cumulative Logistic PCA modelssummary.clpca
Summarizing an Extended Steretype Modelsummary.esm
Summarizing Logistic MDU modelssummary.lmdu
Summarizing Logistic PCA modelssummary.lpca
Summarizing Multinomial Logistic Unfolding model The function summary.mru gives a summary from an object from mru()summary.mru
The function twomodedistance computes the two mode (unfolding) distancetwomodedistance