Package: hpa 1.3.4

hpa: Distributions Hermite Polynomial Approximation

Multivariate conditional and marginal densities, moments, cumulative distribution functions as well as binary choice and sample selection models based on the Hermite polynomial approximation which was proposed and described by A. Gallant and D. W. Nychka (1987) <doi:10.2307/1913241>.

Authors:Bogdan Potanin [aut, cre, ctb], Sofiia Dolgikh [ctb]

hpa_1.3.4.tar.gz
hpa_1.3.4.zip(r-4.7)hpa_1.3.4.zip(r-4.6)hpa_1.3.4.zip(r-4.5)
hpa_1.3.4.tgz(r-4.6-x86_64)hpa_1.3.4.tgz(r-4.6-arm64)hpa_1.3.4.tgz(r-4.5-x86_64)hpa_1.3.4.tgz(r-4.5-arm64)
hpa_1.3.4.tar.gz(r-4.7-arm64)hpa_1.3.4.tar.gz(r-4.7-x86_64)hpa_1.3.4.tar.gz(r-4.6-arm64)hpa_1.3.4.tar.gz(r-4.6-x86_64)
hpa_1.3.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
hpa/json (API)

# Install 'hpa' in R:
install.packages('hpa', repos = c('https://bogdanpotanin.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

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

openblascpp

2.22 score 5 packages 11 scripts 971 downloads 53 exports 3 dependencies

Last updated from:a6e86ce70d. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK227
linux-devel-x86_64OK191
source / vignettesOK291
linux-release-arm64OK206
linux-release-x86_64OK218
macos-release-arm64OK193
macos-release-x86_64OK339
macos-oldrel-arm64OK148
macos-oldrel-x86_64OK380
windows-develOK232
windows-releaseOK235
windows-oldrelOK205
wasm-releaseOK170

Exports:bsplineCombbsplineEstimatebsplineGeneratecoef.hpaBinarycoef.hpaMLcoef.hpaSelectiondhpadhpa0dhpaDiffdhsadnorm_paralleldtrhpaehpaehpaDiffehsaetrhpahpaBinaryhpaMLhpaSelectionihpaihpaDiffitrhpalogLik.hpaBinarylogLik.hpaMLlogLik.hpaSelectionmecdfnormalMomentphpaphpa0plot.hpaBinaryplot.hpaMLplot.hpaSelectionpnorm_parallelpolynomialIndexpredict.hpaBinarypredict.hpaMLpredict.hpaSelectionprint.hpaBinaryprint.hpaMLprint.hpaSelectionprint.summary.hpaBinaryprint.summary.hpaMLprint.summary.hpaSelectionprintPolynomialqhparhpasummary.hpaBinarysummary.hpaMLsummary.hpaSelectiontruncatedNormalMomentvcov.hpaBinaryvcov.hpaMLvcov.hpaSelection

Dependencies:RcppRcppArmadilloRcppParallel

Readme and manuals

Help Manual

Help pageTopics
B-splines generation, estimation and combinationbspline bsplineComb bsplineEstimate bsplineGenerate
Extract coefficients from a hpaBinary objectcoef.hpaBinary
Extract coefficients from a hpaML objectcoef.hpaML
Extract coefficients from a hpaSelection objectcoef.hpaSelection
Calculate normal pdf in paralleldnorm_parallel
Semi-nonparametric single index binary choice model estimationhpaBinary
Probabilities and Moments Hermite Polynomial Approximationdhpa dhpaDiff dtrhpa ehpa ehpaDiff etrhpa hpaDist ihpa ihpaDiff itrhpa phpa qhpa rhpa
Fast pdf and cdf for standardized univariate PGN distributiondhpa0 hpaDist0 phpa0
Semi-nonparametric maximum likelihood estimationhpaML
Perform semi-nonparametric selection model estimationhpaSelection
Probabilities and Moments Hermite Spline Approximationdhsa ehsa hsaDist
Calculates log-likelihood for "hpaBinary" objectlogLik_hpaBinary
Calculates log-likelihood for "hpaML" objectlogLik_hpaML
Calculates log-likelihood for "hpaSelection" objectlogLik_hpaSelection
Calculates log-likelihood for "hpaBinary" objectlogLik.hpaBinary
Calculates log-likelihood for "hpaML" objectlogLik.hpaML
Calculates log-likelihood for "hpaSelection" objectlogLik.hpaSelection
Calculates multivariate empirical cumulative distribution functionmecdf
Calculate k-th order moment of normal distributionnormalMoment
Plot the approximated density of hpaBinary random errorsplot.hpaBinary
Plot approximated marginal density using hpaML outputplot.hpaML
Plot hpaSelection random errors approximated densityplot.hpaSelection
Calculate normal cdf in parallelpnorm_parallel
Multivariate Polynomial RepresentationpolynomialIndex printPolynomial
Predict method for hpaBinarypredict_hpaBinary
Predict method for hpaMLpredict_hpaML
Predict outcome and selection equation values from hpaSelection modelpredict_hpaSelection
Predict method for hpaBinarypredict.hpaBinary
Predict method for hpaMLpredict.hpaML
Predict outcome and selection equation values from hpaSelection modelpredict.hpaSelection
Summary for hpaBinary outputprint_summary_hpaBinary
Summary for hpaML outputprint_summary_hpaML
Summary for hpaSelection outputprint_summary_hpaSelection
Print method for "hpaBinary" objectprint.hpaBinary
Print method for "hpaML" objectprint.hpaML
Print method for "hpaSelection" objectprint.hpaSelection
Summary for "hpaBinary" objectprint.summary.hpaBinary
Summary for hpaML outputprint.summary.hpaML
Summary for "hpaSelection" objectprint.summary.hpaSelection
Summarizing hpaBinary Fitssummary_hpaBinary
Summarizing hpaML Fitssummary_hpaML
Summarizing hpaSelection Fitssummary_hpaSelection
Summarizing hpaBinary Fitssummary.hpaBinary
Summarizing hpaML Fitssummary.hpaML
Summarizing hpaSelection Fitssummary.hpaSelection
Calculate k-th order moment of truncated normal distributiontruncatedNormalMoment
Extract covariance matrix from a hpaBinary objectvcov.hpaBinary
Extract covariance matrix from a hpaML objectvcov.hpaML
Extract covariance matrix from a hpaSelection objectvcov.hpaSelection