Package: lg 0.4.1

lg: Locally Gaussian Distributions: Estimation and Methods

An implementation of locally Gaussian distributions. It provides methods for implementing locally Gaussian multivariate density estimation, conditional density estimation, various independence tests for iid and time series data, a test for conditional independence and a test for financial contagion.

Authors:Håkon Otneim [aut, cre]

lg_0.4.1.tar.gz
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lg_0.4.1.tgz(r-4.6-any)lg_0.4.1.tgz(r-4.5-any)
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lg_0.4.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
lg/json (API)
NEWS

# Install 'lg' in R:

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

Bug tracker:https://github.com/hotneim/lg/issues

On CRAN:

4.18 score 4 stars 25 scripts 151 downloads 3 mentions 19 exports 52 dependencies

Last updated from:458b38dabd. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	126
source / vignettes	OK	173
linux-release-x86_64	OK	129
macos-release-arm64	OK	213
macos-oldrel-arm64	OK	187
windows-devel	OK	89
windows-release	OK	89
windows-oldrel	OK	85
wasm-release	OK	107

Exports:bw_select bw_select_cv_bivariate bw_select_cv_trivariate bw_select_cv_univariate bw_select_plugin_multivariate bw_select_plugin_univariate bw_simple ci_test clg cont_test corplot dlg dlg_bivariate dlg_marginal dlg_marginal_wrapper dlg_trivariate ind_test lg_main partial_cor

Dependencies:boot cli codetools cpp11 cubature curl farver FNN foreach ggplot2 glue gtable isoband iterators jsonlite kernlab KernSmooth ks labeling lattice lifecycle localgauss logspline MASS Matrix MatrixModels matrixStats mclust mgcv multicool mvtnorm nlme np pracma quadprog quantmod quantreg R6 RColorBrewer Rcpp rlang S7 scales SparseM survival tseries TTR vctrs viridisLite withr xts zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Generate sample from a conditional density estimate	accept_reject
Bandwidth selection for local Gaussian correlation.	bw_select
Cross-validation for bivariate distributions	bw_select_cv_bivariate
Cross-validation for trivariate distributions	bw_select_cv_trivariate
Cross-validation for univariate distributions	bw_select_cv_univariate
Plugin bandwidth selection for multivariate data	bw_select_plugin_multivariate
Plugin bandwidth selection for univariate data	bw_select_plugin_univariate
Create simple bandwidth object	bw_simple
Check bandwidth vector	check_bw_bivariate
Check bw method	check_bw_method
Check bandwidth vector	check_bw_trivariate
Check the data and grid	check_data
Check the arguments for the 'dmvnorm_wrapper' function	check_dmvnorm_arguments
Check estimation method	check_est_method
Check that an object has class "lg"	check_lg
Test for conditional independence	ci_test
Calculate the value of the test statistic for the conditional independence test	ci_test_statistic
The locally Gaussian conditional density estimator	clg
Test for financial contagion	cont_test
Plot local correlation maps	corplot
The locally Gaussian density estimator (LGDE)	dlg
Bivariate density estimation	dlg_bivariate
Marginal density estimation	dlg_marginal
Marginal estimates for multivariate data	dlg_marginal_wrapper
Trivariate density estimation	dlg_trivariate
Wrapper for 'dmvnorm'	dmvnorm_wrapper
Wrapper for 'dmvnorm' - single point	dmvnorm_wrapper_single
Auxiliary function for calculating the asymptotic standard deviations for the local Gaussian correlations	gradient
Independence tests	ind_test
Function that calculates the test statistic in the independence tests.	ind_teststat
Interpolate a univariate conditional density function	interpolate_conditional_density
'lg': A package for calculating the local Gaussian correlation in multivariate applications.	lg
Create an 'lg' object	lg_main
Calculate the local conditional covariance between two variables	local_conditional_covariance
Auxiliary function for calculating the asymptotic standard deviations for the local Gaussian correlations	make_C
Evaluate the multivariate normal	mvnorm_eval
Calculate the local Gaussian partial correlation	partial_cor
Bootstrap replication under the null hypothesis	replicate_under_ci
Transform the marginals of a multivariate data set to standard normality based on the logspline density estimator (Kooperberg and Stone, 1991). See Otneim and Tjøstheim (2017) for details.	trans_normal
Auxiliary function for calculating the local score function u	u