Package: gamlss.prepdata 0.1.19

Mikis Stasinopoulos

gamlss.prepdata: Prepering Data for Fitting a Generalized Additive Model for Location Scale and Shape

Functions for prepering data to fit a Generalized Additive Models for Location Scale and Shape from the 'gamlss' or `gamlss2` package, Stasinopoulos and Rigby (2007) <doi:10.18637/jss.v023.i07>, using for graphical methods 'ggplot2'.

Authors:Mikis Stasinopoulos [aut, cre, cph], Robert Rigby [aut], Fernanda De Bastiani [aut], Julian Merder [ctb]

gamlss.prepdata_0.1.19.tar.gz
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manual.pdf |manual.html✨
card.svg |card.png
gamlss.prepdata/json (API)
NEWS

# Install 'gamlss.prepdata' in R:

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

Bug tracker:https://github.com/gamlss-dev/gamlss.prepdata/issues

On CRAN:

3.81 score 3 scripts 113 mentions 59 exports 91 dependencies

Last updated from:50496dc386. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	184
source / vignettes	OK	220
linux-release-x86_64	OK	161
macos-release-arm64	OK	114
macos-oldrel-arm64	OK	122
windows-devel	OK	129
windows-release	OK	136
windows-oldrel	OK	142
wasm-release	OK	129

Exports:ACE ACE.iter cor_boot cor_perm_test data_association data_boot_index data_boot_weights data_bucket data_cha2fac data_cor data_cut data_dim data_distinct data_exclude_class data_fac2num data_factor data_few2fac data_form2X data_formulae data_int2num data_inter data_Kfold_index data_Kfold_weights data_leverage data_mcor data_na_obs data_na_vars data_names data_omit data_only_continuous data_outliers data_part data_part_list data_pcor data_plot data_rename data_response data_rm data_rm1val data_rmNAvars data_scale data_select data_shorter_names data_str data_vars2data data_void data_xyplot data_zscores high_val low_val mcor void y_distinct y_factor y_outliers y_outliers_both y_outliers_by y_outliers_z y_zscores

Dependencies:acepack askpass base64enc bslib cachem cli codetools cpp11 crosstalk curl data.table digest distributions3 doParallel dplyr ellipse evaluate farver fastmap fontawesome foreach Formula fs gamlss gamlss.data gamlss.dist gamlss.foreach gamlss.ggplots gamlss.inf gamlss2 generics ggplot2 ggridges glmnet glue gtable highr htmltools htmlwidgets httr isoband iterators jquerylib jsonlite knitr labeling later lattice lazyeval lifecycle magrittr MASS Matrix memoise mgcv mime mvtnorm nlme openssl otel pillar pkgconfig plotly promises purrr R6 rappdirs RColorBrewer Rcpp RcppEigen rlang rmarkdown S7 sass scales shape stringi stringr survival sys tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun yaImpute yaml

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Prepering Data for Fitting a Generalized Additive Model for Location Scale and Shape	gamlss.prepdata-package gamlss.prepdata
Pemutatione and Boostrap Tests for Paitwise EReletionships	cor_boot cor_perm_test
Plotting pairwise linear and partial correlations.	data_association data_cor data_pcor high_val low_val
Function to get information from data.	data_dim data_names data_na_obs data_na_vars data_omit data_rename data_shorter_names
Changing the Reference Level of Factors	data_factor y_factor
Identifying Pair-Wise Interactions in the Data Frames	data_inter
Getting the Leverage of All explanatory variables	data_leverage
Functions to fit Maximal Correlation to Data	ACE ACE.iter data_mcor mcor
Outlier identification	data_outliers y_outliers y_outliers_both y_outliers_by y_outliers_loop y_outliers_z
A function to partition a data frame	data_boot_index data_boot_weights data_cut data_Kfold_index data_Kfold_weights data_part data_part_list
Functions operating on variables in the data	data_exclude_class data_only_continuous data_rm data_rm1val data_rmNAvars data_select
Scalling Continuous Variables in Data	data_form2X data_formulae data_scale data_vars2data
Function applied to data	data_cha2fac data_distinct data_fac2num data_few2fac data_int2num data_str y_distinct
Finding the Percentage of Empty Spaces	data_void void
Plotting the response against the explanatory variables	data_bucket data_plot data_response data_xyplot data_zscores y_zscores