Designed to simplify and streamline the process of reading
    and processing large volumes of data in R, this package offers a
    collection of functions tailored for bulk data operations. It enables
    users to efficiently read multiple sheets from Microsoft Excel and
    Google Sheets workbooks, as well as various CSV files from a
    directory. The data is returned as organized data frames, facilitating
    further analysis and manipulation. Ideal for handling extensive data
    sets or batch processing tasks, bulkreadr empowers users to manage
    data in bulk effortlessly, saving time and effort in data preparation
    workflows. Additionally, the package seamlessly works with labelled
    data from SPSS and Stata.

Provides functionality to automatically detect groove locations via a Bayesian changepoint detection method to be used in the data preprocessing step
    of forensic bullet matching algorithms. The methods in this package are based on those in Stephens (1994) <doi:10.2307/2986119>. Bayesian changepoint detection will simply be an option
    in the function from the package 'bulletxtrctr' which identifies the groove locations.

Analyze bullet lands using nonparametric methods. We provide a
    reading routine for x3p files (see <http://www.openfmc.org> for more
    information) and a host of analysis functions designed to assess the
    probability that two bullets were fired from the same gun barrel.

Stock, Options and Futures Trading Strategies for Traders and Investors with Bullish Outlook are represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).