Converts TXT and XML data curated by the United States Patent and
    Trademark Office (USPTO). Allows conversion of bulk data after downloading
    directly from the USPTO bulk data website, eliminating need for users to
    wrangle multiple data formats to get large patent databases in tidy,
    rectangular format. Data details can be found on the USPTO website
    <https://bulkdata.uspto.gov/>. Currently, all 3 formats: 1. TXT data
    (1976-2001); 2. XML format 1 data (2002-2004); and 3. XML format 2 data
    (2005-current) can be converted to rectangular, CSV format.
    Relevant literature that uses data from USPTO includes Wada (2020)
    <doi:10.1007/s11192-020-03674-4> and Plaza & Albert (2008)
    <doi:10.1007/s11192-007-1763-3>.

Provides functions to simplify the 'PatentsView' API
    (<https://patentsview.org/apis/purpose>) query language,
    send GET and POST requests to the API's seven endpoints, and parse the data
    that comes back.

R's implementation of the JavaScript library 'path-to-regexp',
 it aims to provide R web frameworks features such as parameter handling 
 among other URL path utilities.

Facilitates the performance of several analyses, including simple and sequential path coefficient analysis, correlation estimate, drawing correlogram, Heatmap, and path diagram. When working with raw data, that includes one or more dependent variables along with one or more independent variables are available, the path coefficient analysis can be conducted. It allows for testing direct effects, which can be a vital indicator in path coefficient analysis. The process of preparing the dataset rule is explained in detail in the vignette file "Path.Analysis_manual.Rmd". You can find this in the folders labelled "data" and "~/inst/extdata". Also see: 1)the 'lavaan', 2)a sample of sequential path analysis in 'metan' suggested by Olivoto and Lúcio (2020) <doi:10.1111/2041-210X.13384>, 3)the simple 'PATHSAS' macro written in 'SAS' by Cramer et al. (1999) <doi:10.1093/jhered/90.1.260>, and 4)the semPlot() function of 'OpenMx' as initial tools for conducting path coefficient analyses and SEM (Structural Equation Modeling). To gain a comprehensive understanding of path coefficient analysis, both in theory and practice, see a 'Minitab' macro developed by Arminian, A. in the paper by Arminian et al. (2008) <doi:10.1080/15427520802043182>.