Package: nspmix 2.0-0

nspmix: Nonparametric and Semiparametric Mixture Estimation

Mainly for maximum likelihood estimation of nonparametric and semiparametric mixture models, but can also be used for fitting finite mixtures. The algorithms are developed in Wang (2007) <doi:10.1111/j.1467-9868.2007.00583.x> and Wang (2010) <doi:10.1007/s11222-009-9117-z>.

Authors:Yong Wang [aut, cre]

nspmix_2.0-0.tar.gz
nspmix_2.0-0.zip(r-4.7-any)nspmix_2.0-0.zip(r-4.6-any)nspmix_2.0-0.zip(r-4.5-any)
nspmix_2.0-0.tgz(r-4.6-any)nspmix_2.0-0.tgz(r-4.5-any)
nspmix_2.0-0.tar.gz(r-4.7-any)nspmix_2.0-0.tar.gz(r-4.6-any)
nspmix_2.0-0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
nspmix/json (API)

# Install 'nspmix' in R:
install.packages('nspmix', repos = c('https://yong3738.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

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

1.15 score 14 scripts 212 downloads 42 exports 1 dependencies

Last updated from:fdabf32b8e. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK136
source / vignettesOK179
linux-release-x86_64OK164
macos-release-arm64OK123
macos-oldrel-arm64OK99
windows-develOK93
windows-releaseOK71
windows-oldrelOK79
wasm-releaseOK92

Exports:cnmcnmapcnmmscnmplcvpsdiscdmixdnpgeomdnpnbinomdnpnormdnppoisgridpointshcnminitialinitial0llexllexdblogdloglikmlogitnpgeomnpnbinomnpnormnppoisplot.nspmixplotgradpnpgeompnpnbinompnpnormpnppoisprint.cvpsrcvprcvpsrmlogitrnpgeomrnpnbinomrnpnormrnppoissuppspacevalidweightwhist

Dependencies:lsei

Readme and manuals

Help Manual

Help pageTopics
Beta-blockers Databetablockers
Z-values of BRCA Databrca
Maximum Likelihood Estimation of a Nonparametric Mixture Modelcnm
Maximum Likelihood Estimation of a Semiparametric Mixture Modelcnmap cnmms cnmpl
Class 'cvps'cvp cvps print.cvps rcvp rcvps
Class 'disc'disc
Density function of a mixture distributiondmix
Grid pointsgridpoints
Hierarchical Constrained Newton methodhcnm
Initialization for a nonparametric/semiparametric mixtureinitial
Initialisationinitial0
Log-likleihood Extra Term.llex
Derivative of the log-likleihood Extra Termllexdb
Log-density and its derivative valueslogd
Log-likelihood value of a mixtureloglik
Lung Cancer Datalungcancer
Class 'mlogit'mlogit rmlogit
Class 'npgeom'dnpgeom npgeom pnpgeom rnpgeom
Class 'npnbinom'dnpnbinom npnbinom pnpnbinom rnpnbinom
Class 'npnorm'dnpnorm npnorm pnpnorm rnpnorm
Class 'nppois'dnppois nppois pnppois rnppois
Plot a discrete distribution functionplot.disc
Plotting a nonparametric geometric mixtureplot.npgeom
Plotting a nonparametric negative binomial mixtureplot.npnbinom
Plotting a Nonparametric or Semiparametric Normal Mixtureplot.npnorm
Plotting a nonparametric Poisson mixtureplot.nppois
Plots a function for an object of class 'nspmix'nspmix plot.nspmix
Plot the Gradient Functionplotgrad
Prints a discrete distribution functionprint.disc
Sorting of an Object of Class 'npnorm'sort.npnorm
Sorting of an Object of Class 'nppois'sort.nppois
Support spacesuppspace
Illness Spells and Frequencies of Thai Preschool Childrenthai
Toxoplasmosis Datatoxo
Valid parameter valuesvalid
Weightsweight
Weighted Histograms Plots or computes the histogram with observations with multiplicities/weights. Just like 'hist', 'whist' can either plot the histogram or compute the values that define the histogram, by setting 'plot' to 'TRUE' or 'FALSE'. The histogram can either be the one for frequencies or density, by setting 'freq' to 'TRUE' or 'FALSE'.whist