Package: hpa 1.3.3

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 Hermite polynomial approximation which was proposed and described by A. Gallant and D. W. Nychka (1987) <doi:10.2307/1913241>.

Authors:Potanin Bogdan

hpa_1.3.3.tar.gz
hpa_1.3.3.zip(r-4.5)hpa_1.3.3.zip(r-4.4)hpa_1.3.3.zip(r-4.3)
hpa_1.3.3.tgz(r-4.5-x86_64)hpa_1.3.3.tgz(r-4.5-arm64)hpa_1.3.3.tgz(r-4.4-x86_64)hpa_1.3.3.tgz(r-4.4-arm64)hpa_1.3.3.tgz(r-4.3-x86_64)hpa_1.3.3.tgz(r-4.3-arm64)
hpa_1.3.3.tar.gz(r-4.5-noble)hpa_1.3.3.tar.gz(r-4.4-noble)
hpa_1.3.3.tgz(r-4.4-emscripten)hpa_1.3.3.tgz(r-4.3-emscripten)
hpa.pdf |hpa.html✨
hpa/json (API)

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

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

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

openblascppopenmp

2.12 score 4 packages 11 scripts 498 downloads 53 exports 3 dependencies

Last updated 1 years agofrom:de4886440c. Checks:11 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 01 2025
R-4.5-win-x86_64OKFeb 01 2025
R-4.5-mac-x86_64OKFeb 01 2025
R-4.5-mac-aarch64OKFeb 01 2025
R-4.5-linux-x86_64OKFeb 01 2025
R-4.4-win-x86_64OKFeb 01 2025
R-4.4-mac-x86_64OKFeb 01 2025
R-4.4-mac-aarch64OKFeb 01 2025
R-4.3-win-x86_64OKFeb 01 2025
R-4.3-mac-x86_64OKFeb 01 2025
R-4.3-mac-aarch64OKJan 02 2025

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 hpaBinary objectcoef.hpaBinary
Extract coefficients from hpaML objectcoef.hpaML
Extract coefficients from 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 hpaBinary random errors approximated densityplot.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 hpaBinary objectvcov.hpaBinary
Extract covariance matrix from hpaML objectvcov.hpaML
Extract covariance matrix from hpaSelection objectvcov.hpaSelection