Package: Dyn4cast 11.11.26

Dyn4cast: Dynamic Modeling and Machine Learning Environment

Estimates, predict and forecast dynamic models as well as Machine Learning metrics which assists in model selection for further analysis. The package also have capabilities to provide tools and metrics that are useful in machine learning and modeling. For example, there is quick summary, percent sign, Mallow's Cp tools and others. The ecosystem of this package is analysis of economic data for national development. The package is so far stable and has high reliability and efficiency as well as time-saving. The package is a variety but the following references are important guide to the major themes in the package (Hyndman & Athanasopoulos (2014 ISBN 978-0-9875071-0-5), Alkire & Santos (2014, doi.org/10.1016/j.worlddev.2014.01.026)).

Authors:Job Nmadu [aut, cre]

Dyn4cast_11.11.26.tar.gz
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Dyn4cast_11.11.26.tgz(r-4.6-any)Dyn4cast_11.11.26.tgz(r-4.5-any)
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Dyn4cast_11.11.26.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
Dyn4cast/json (API)

# Install 'Dyn4cast' in R:
install.packages('Dyn4cast', repos = c('https://jobnmadu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jobnmadu/dyn4cast/issues

Pkgdown/docs site:https://jobnmadu.github.io

Datasets:
  • COVID19 - Dynamic Forecast of Five Models and their Ensembles
  • Data - Collection of Machine Learning Model Metrics for Easy Reference
  • garrett_data - Garrett Ranking of Categorical Data
  • garrett_table - Garrett Ranking of Categorical Data
  • linearsystems - Linear Model and various Transformations for Efficiency
  • mdpi1 - Sequential Computation of Dynamic Multidimensional Indices
  • mdpi2 - Sequential Computation of Dynamic Multidimensional Indices
  • Quicksummary - Quick Formatted Summary of Machine Learning Data
  • sampling - Linear Model and various Transformations for Efficiency
  • Transform - Standardize 'data.frame' for comparable *Machine Learning* prediction and visualization
  • treatments - Enhanced Estimation of Treatment Effects of Binary Data from Randomized Experiments

On CRAN:

Conda:

data-scienceequal-lenght-forecastforecastingknotsmachine-learningnigeriapredictionregression-modelsspline-modelsstatisticstime-series

5.95 score 5 stars 54 scripts 20 exports 71 dependencies

Last updated from:98b1d7719f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK300
source / vignettesOK278
linux-release-x86_64OK298
macos-release-arm64OK160
macos-oldrel-arm64OK257
windows-develOK249
windows-releaseOK234
windows-oldrelOK224
wasm-releaseOK157

Exports:corplotdata_transformDynamicForecastestimate_plotformattedcutgarrett_rankinggenderindex_constructionLinearsystemsMallowsCpmdiMLMetricsmodel_factorsModel_factorsodds_summaryPercentplot_mdiquicksummaryrelative_likerttreatment_model

Dependencies:backportsbase64encbayestestRbslibcachemcheckmateclicorrplotcpp11data.tabledatawizarddigestdplyrevaluatefarverfastmapfontawesomeformattableFormulafsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetsinsightisobandjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrmarginaleffectsmemoiseMetricsmimeModelMetricsmodelsummaryparametersperformancepillarpkgconfigpurrrR6rappdirsRColorBrewerRcpprlangrmarkdownS7sassscalesstringistringrtablestibbletidyrtidyselecttinytabletinytexutf8vctrsviridisLitewithrxfunyamlzoo

Dynamic Modeling and Machine Learning Environment
Introduction | Installation | data_transform: Standardize data.frame for comparable Machine Learning prediction and visualization | Examples | View the data without transformation | Transformation by min-max method | log transformation | Mean-SD transformation | DynamicForecast: Dynamic Forecast of Five Models and their Ensembles | Example | Twenty eight points less than full length of data | Fourteen points less than full length of data | formattedcut: Convert continuous vector variable to formatted factors | garrett_ranking: Garrett Ranking of Categorical Data | Ranking is supplied | Ranking not supplied | Rank subset of the data | gender: Create Gender Variable | Linearsystems: Linear Model and various Transformations for Efficiency | Estimation Without test data, 14 models | Estimation Without test data, polynomial models | Estimation With test data, linear models | Estimation With test data, power models | Estimation With test data, root models | Estimation Without test data, inverse models | mdi: Sequential Computation of Dynamic Multidimensional Indices (MDI) | With three dimensions and factor | With three dimensions, no factor | With four dimensions and factor | With five dimensions and factor | With five dimensions, no plot | With five dimensions, no factor, no plot | With six dimensions and factor | With seven dimensions and factor | With eight dimensions and factor | With nine dimensions and factor | MLMetrics: Collection of Machine Learning Model Metrics for Easy Reference | model_factors: Latent Factors Recovery from Variables Loadings | Percent: Attach Per Cent Sign to Data | A vector data | Data frame | quicksummary: Quick Formatted Summary of Machine Learning Data | Likert-type data | Continuous data | treatment_model: Enhanced Estimation of Treatment Effects of Binary Data from Randomized Experiments | index_construction: Index Construction for estimation of Exposure or Sensitivity | relative_likert: Convert Likert Data to Relative Scores and knowledge-based Adaptive Capacity | odds_summary: Odds-Based Measures for Binary and Categorical Models
Rendered fromDyn4cast.Rmdusingknitr::rmarkdown

Last update: 2026-07-08
Started: 2021-02-17

Getting started with Dyn4cast
Installation | Suggested packages | Citation
Rendered fromstarting.Rmdusingknitr::rmarkdown

Last update: 2026-07-08
Started: 2025-05-23