Version 0.2.0 of the coronavirus R data package was pushed today to CRAN. The coronavirus package provides a tidy format for Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus dataset. Version 0.2.0 catch up with the significant changes in the data that took place since the initial release on February 24, changing the package status from experimental to maturing.

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Last week I pushed an update of the covid19italy package to CRAN (v0.2.0). The covid19italy R package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) pandemic outbreak in Italy. The package includes the following three datasets: italy_total - daily summary of the outbreak on the national level italy_region - daily summary of the outbreak on the region level italy_province - daily summary of the outbreak on the province level More details about the datasets available on the following vignette

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I am pleased to announce a new R package - forecastLM. The package, as the name implies, provides applications for forecasting regular time series data with a linear regression model (based on the lm function from the stats package). It supports both ts and tsibble objects as inputs and enables simple extractions of features from the input object on the fly. Example for such features:

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TSstudio 0.1.5 on CRAN

A new version (0.1.5) of the TSstudio package was pushed to CRAN last month. The release includes new functions as well as bug fixing, and update of the package license (modified from GPL-3 to MIT). New features train_model - a flexible framework for training, testing, evaluating, and forecasting models. This function provides the ability to run multiple models with backtesting or single training/testing partitions. This function will replace the ts_backtesting function which will deprecated in the next release.

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TSstudio 0.1.3

I used the Thanksgiving break to push a new update of the TSstudio package to CRAN (version 0.1.3). The new version includes an update for the ts_backtesting function along with two new function - ts_to_prophet for converting time series objects to a prophet input format (i.e., ds and y columns), and ccf_plot for lags plot between two time series. The package can be installed from either CRAN or Github:

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TSstudio 0.1.2

Happy to announce the release of TSstudio 0.1.2 to CRAN. The TSstudio package provides tools for descriptive and predictive analysis of time series data, utilizing the visualization enegin of the plotly package and forecasting models from the forecast, forecastHybrid and bsts packages. Installation Install the stable version from CRAN: install.packages("TSstudio") or install the development version from Github: # install.packages("devtools") devtools::install_github("RamiKrispin/TSstudio") New features The new release includes new set of functions for forecasting automation with the use of backtesting and ‘horse race’ approach, forecast visualization, quantile plot of time series data, and new datasets.

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Rami Krispin

Time series analysis, forecasting, machine learning, and data visualization

Data scientist

Cupertino, CA