Package: fDMA 2.2.7

fDMA: Dynamic Model Averaging and Dynamic Model Selection for Continuous Outcomes

Allows to estimate dynamic model averaging, dynamic model selection and median probability model. The original methods are implemented, as well as, selected further modifications of these methods. In particular the user might choose between recursive moment estimation and exponentially moving average for variance updating. Inclusion probabilities might be modified in a way using 'Google Trends'. The code is written in a way which minimises the computational burden (which is quite an obstacle for dynamic model averaging if many variables are used). For example, this package allows for parallel computations and Occam's window approach. The package is designed in a way that is hoped to be especially useful in economics and finance. Main reference: Raftery, A.E., Karny, M., Ettler, P. (2010) <doi:10.1198/TECH.2009.08104>.

Authors:Krzysztof Drachal [aut, cre]

fDMA_2.2.7.tar.gz
fDMA_2.2.7.zip(r-4.5)fDMA_2.2.7.zip(r-4.4)fDMA_2.2.7.zip(r-4.3)
fDMA_2.2.7.tgz(r-4.4-x86_64)fDMA_2.2.7.tgz(r-4.4-arm64)fDMA_2.2.7.tgz(r-4.3-x86_64)fDMA_2.2.7.tgz(r-4.3-arm64)
fDMA_2.2.7.tar.gz(r-4.5-noble)fDMA_2.2.7.tar.gz(r-4.4-noble)
fDMA_2.2.7.tgz(r-4.4-emscripten)fDMA_2.2.7.tgz(r-4.3-emscripten)
fDMA.pdf |fDMA.html✨
fDMA/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/kdrachal/fdma/issues

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

On CRAN:

4.26 score 3 stars 61 scripts 319 downloads 24 exports 59 dependencies

Last updated 1 years agofrom:cb3ce0cf60. Checks:OK: 4 NOTE: 5. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 29 2024
R-4.5-win-x86_64NOTEOct 29 2024
R-4.5-linux-x86_64NOTEOct 29 2024
R-4.4-win-x86_64NOTEOct 29 2024
R-4.4-mac-x86_64NOTEOct 29 2024
R-4.4-mac-aarch64NOTEOct 29 2024
R-4.3-win-x86_64OKOct 29 2024
R-4.3-mac-x86_64OKOct 29 2024
R-4.3-mac-aarch64OKOct 29 2024

Exports:altfaltf2altf3altf4archtestdescstatdmtestfDMAgNormalizegrid.DMAgrid.roll.reggrid.tvphit.ratiohmdmtestmdmtestnormalizeonevarrec.regreduce.sizeroll.regrvistandardizestesttvp

Dependencies:bitopscaToolsclicodetoolscolorspacecurldoParallelfansifarverforeachforecastfracdiffgenericsggplot2glueGPArotationgplotsgtablegtoolsisobanditeratorsitertoolsjsonliteKernSmoothlabelinglatticelifecyclelmtestmagrittrMASSMatrixmgcvmnormtmunsellnlmennetpillarpkgconfigpngpsychquadprogquantmodR6RColorBrewerRcppRcppArmadillorlangscalestibbletimeDatetseriesTTRurcautf8vctrsviridisLitewithrxtszoo

fDMA

Rendered fromfDMA_vignette.pdf.asisusingR.rsp::asison Oct 29 2024.

Last update: 2020-04-27
Started: 2020-04-27

Readme and manuals

Help Manual

Help pageTopics
Computes a Few Alternative Forecasts.altf
Computes a Few Alternative Forecasts Based on Model Averaging.altf2
Computes a Rolling Regression Averaged over Different Window Sizes.altf3
Computes a Time-Varying Parameters Rolling Regression Averaged over Different Window Sizes.altf4
Computes Engle's ARCH Test.archtest
Extracts Averaged Coefficients from 'dma' Model.coef coef.dma
Crude Oil Data.crudeoil
Computes Basic Descriptive Statistics.descstat
Computes Diebold-Mariano Test.dmtest
Computes Dynamic Model Averaging.fDMA
Extracts Fitted Values from 'dma' Model.fitted fitted.dma
Normalizes a Numeric Matrix by Rows.gNormalize
Computes 'fDMA' Function for Multiple Values of 'alpha' and 'lambda'.grid.DMA
Computes 'roll.reg' Function for Multiple Values of 'window'.grid.roll.reg
Computes 'tvp' Function for Multiple Values of 'lambda'.grid.tvp
Computes Hit Ratio (HR) for Forecast.hit.ratio
Computes Diebold-Mariano Test when Presence of ARCH Effects is Suspected.hmdmtest
Computes Harvey-Leybourne-Newbold Test.mdmtest
Normalizes a Numeric Matrix by Columns.normalize
Creates a 'matrix' of one-variable models.onevar
Plots Selected Outcomes from 'altf' Object.plot.altf
Plots Selected Outcomes from 'altf2' Object.plot.altf2
Plots Selected Outcomes from 'altf3' Object.plot.altf3
Plots Selected Outcomes from 'altf4' Object.plot.altf4
Plots Selected Outcomes from 'fDMA' Function.plot plot.dma
Plots Selected Outcomes from 'grid.DMA' Function.plot.grid.dma
Plots Selected Outcomes from 'grid.roll.reg' Function.plot.grid.roll.reg
Plots Selected Outcomes from 'grid.tvp' Function.plot.grid.tvp
Plots Selected Outcomes from 'reg' Object.plot.reg
Plots Selected Outcomes from 'tvp' Object.plot.tvp
Computes Predictions from 'dma' Model.predict predict.dma
Prints 'altf' Object.print.altf
Prints 'altf2' Object.print.altf2
Prints 'altf3' Object.print.altf3
Prints 'altf4' Object.print.altf4
Prints 'dma' Object.print print.dma
Prints 'grid.dma' Object.print.grid.dma
Prints 'grid.roll.reg' Object.print.grid.roll.reg
Prints 'grid.tvp' Object.print.grid.tvp
Prints 'reg' Object.print.reg
Prints 'tvp' Object.print.tvp
Computes Recursive Regression.rec.reg
Reduces the Size of 'fDMA' or 'grid.DMA' Outcomes.reduce.size
Extracts Residuals from 'dma' Model.residuals residuals.dma
Computes Rolling Regression.roll.reg
Extracts Relative Variable Importances from 'fDMA' Model.rvi
Standardizes a Numeric Matrix by Columns.standardize
Computes a Few Stationarity Tests.stest
Summarizes Outcomes from 'altf' Object.summary.altf
Summarizes Outcomes from 'altf2' Object.summary.altf2
Summarizes Outcomes from 'altf3' Object.summary.altf3
Summarizes Outcomes from 'altf4' Object.summary.altf4
Summarizes Outcomes from 'dma' Object.summary summary.dma
Summarizes Outcomes from 'grid.dma' Objects.summary.grid.dma
Summarizes Outcomes from 'grid.roll.reg' Objects.summary.grid.roll.reg
Summarizes Outcomes from 'grid.tvp' Objects.summary.grid.tvp
Summarizes Outcomes from 'reg' Object.summary.reg
Summarizes Outcomes from 'tvp' Object.summary.tvp
Google Trends for Crude Oil Data.trends
Computes Time-Varying Parameters Regression.tvp