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The Dyn4cast package is designed to be a lightweight package. The philosophy behind it was the need to provide quick updates and visualization of Nigerian COVID-19 cases during the pandemic in 2020, hence had only one function, DynamicForecast. The aim was to simplify the estimation, prediction and forecast of time-varying COVID-19 dataset, based on daily update of incidences. There was need to have a function which is able to handle continuous data collection, estimation and forecast. That was what led to the name of the package. From that single function, the number of functions have grown to more than 10 because there were need to have supporting functions to make the forecasting easy. However, the package has pride itself of the line line technology by providing various machine learning functions that have the functionality of revealing the facts behind your data. The functions are working optimally and efforts are continuously being made to improve on them and ensuring that dependencies are reduced to the barest minimum. The unique selling point of this package is that it takes away the need to load multiple libraries to perform the various machine learning tasks.
Although the released version of Dyn4cast could be installed from CRAN, the plans to submit it to CRAN is underway. Therefore, only the development version is currently running. The canonical form for CRAN is:
install.packages("Dyn4cast")The development version is the only one available now and can be installed from GitHub with:
# install.packages("devtools")
pak::pak("JobNmadu/Dyn4cast")The development version can also be installed through r-universe. Use the form:
install.packages("Dyn4cast", repos = c("https://jobnmadu.r-universe.dev", "https://cloud.r-project.org"))At present, the package exports the following functions:
DynamicForecast which takes two required arguments:
the Data of any recognized format but should be a
dataframe containing two columns Date and
Case. The Date is the day/month/year the data is
collected while Case is the variable for forecasting. The Date must be
in the recognized format i.e. ‘YYYY-MM-DD’. The other arguments parsed
to the function are MaximumDate, which is the last date
Data was collected and BREAKS, which is a vector of numbers
and is used as knots in estimating spline
polynomials.
Percent which affix the % sign on a value or a
vector or data frame of values.
MLMetrics which collects more than 40 metrics that
are useful in model selection. The beauty of this function is the
simplicity with which these metrics are collected from difference
packages and saves the user the need to load more than 10 libraries in
order to get these metrics.
MallowsCP for determining the the Mallows
CP.
Linearsystems for linear regression model with some
transformation.
quicksummary which outputs a formatted table of
useful summary statistics of machine learning data.
formattedcut is a wrapper for providing publication
ready frequency tables for continuous variable.
data_transform is a wrapper for standardizing
data.frame to make the values comparable for estimation
and/or visualization.
estimate_plot is a function for plotting estimated
coefficients of a model in their order of significance.
corplot is for plotting the correlation
matrix.
garrett_ranking is for ranking Likert-type
data.
Model_factors is for determining and retrieving
latent factors from Likert-type data for estimation and Machine
Learning.
treatment_model is for propensity matching
treatments effects and other metrics in the Machine Learning
Environment.
mdi is for computation of multidimensional
indicators and indices
plot_mdi is to plot the indices
gender is to convert age and sex to gender
index_construction is to convert indicators of
exposure or sensitivity to vector of index
relative_likert converts likert scores to vector of
index for adaptive capacity
odds_summary computes odds ratios, percentage
changes, and confidence intervals from fitted binary and categorical
regression models
The citation information for this package can be obtained easily when
you run citation("Dyn4cast") in your R
console.
citation("Dyn4cast")
#> To cite package 'Dyn4cast' in publications use:
#>
#> Nmadu J (2026). _Dyn4cast: Dynamic Modeling and Machine Learning
#> Environment_. R package version 11.11.26,
#> <https://github.com/JobNmadu/Dyn4cast>.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {Dyn4cast: Dynamic Modeling and Machine Learning Environment},
#> author = {Job Nmadu},
#> year = {2026},
#> note = {R package version 11.11.26},
#> url = {https://github.com/JobNmadu/Dyn4cast},
#> }Although not dependencies, the package derives functionally from a number of other packages and so may require you to install them if not already installed on your machine. Some of the packages are listed below:
install.packages(c("testthat", "tidyverse", "rmarkdown", "covr"
"caret", "kableExtra", "knitr", "spelling", "psych",
"readr", "MetBrewer", "data.table", "ggtext", "lubridate",
"forecast", "MASS", "mlogit", "nnet", "betareg", "mvProbit",
"miscTools"))Note that a warning (not error) is thrown up while
estimating the RMSE for the Ensembled with equal weight
model. It was thoroughly investigated and causes no harm. Efforts are
still on to silence the warning, which I will soon. The warning is one
of such issues that is general to R. If you set your chunk
option to warning = FALSE you will not notice the
warning.
The package is still very much in progress as such feedback, particularly at this developmental stage, would be greatly welcome and appreciated. Please fork your feedback at GitHub.
Mahoney, M. (2021). Model averaging methods: how and why to build ensemble models.
Hyndman, R. J. (2020). Quantile forecasting with ensembles and combinations in Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning, eds. Gilliland, Tashman & Sglavo. John Wiley & Sons.
Hyndman, R.J., & Athanasopoulos, G. (2021). Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed on July 30, 2021.
Lucy D’Agostino McGowan (2019). Understanding propensity score weighting. Available at: https://livefreeordichotomize.com/posts/2019-01-17-understanding-propensity-score-weighting/
Please note that the Dyn4cast project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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