Using the R package 'reticulate', this package creates an interface to the 'pysd' toolset.
    The package provides an R interface to a number of 'pysd' functions, and can read files in
    'Vensim' 'mdl' format, and 'xmile' format. The resulting simulations are returned as a 'tibble', and from 
    that the results can be processed using 'dplyr' and 'ggplot2'. The package has been tested  using 'python3'.

It enables 'sparklyr' to integrate with 'Spark Connect', and
    'Databricks Connect' by providing a wrapper over the 'PySpark' 'python'
    library.

'Google Trends' provides cross-sectional and time-series data on searches, but lacks readily available 
    longitudinal data. Researchers, who want to create longitudinal 'Google Trends' on their own, face practical challenges, such as normalized counts that make it difficult to combine 
    cross-sectional and time-series data and limitations in data formats and timelines that limit data 
    granularity over extended time periods. 
    This package addresses these issues and enables researchers to generate longitudinal 'Google Trends' data.  
    This package is built on 'pytrends', a Python library that acts as the unofficial 'Google Trends API' to collect 'Google Trends' data. As long as the 'Google Trends API', 'pytrends' and all their dependencies are working, this package will work.
    During testing, we noticed that for the same input (keyword, topic, data_format, timeline), the output index can vary from time to time. Besides, if the keyword is not very popular, then the resulting dataset will contain a lot of zeros, which will greatly affect the final result. While this package has no control over the accuracy or quality of 'Google Trends' data, once the data is created, this package coverts it to longitudinal data.  
    In addition, the user may encounter a 429 Too Many Requests error when using cross_section() and time_series() to collect 'Google Trends' data. This error indicates that the user has exceeded the rate limits set by the 'Google Trends API'. For more information about the 'Google Trends API' - 'pytrends', visit <https://pypi.org/project/pytrends/>.