Contains data from the May 2020 Occupational Employment and 
    Wage Statistics data release from the U.S. Bureau of Labor Statistics. The 
    dataset covers employment and wages across occupations, industries, states,  
    and at the national level. Metropolitan data is not included.

Contains data from the May 2021 Occupational Employment and 
    Wage Statistics data release from the U.S. Bureau of Labor Statistics. The 
    dataset covers employment and wages across occupations, industries, states,  
    and at the national level. Metropolitan data is not included.

Allows production of 'Microsoft' corporate documents from 'R
    Markdown' by reusing formatting defined in 'Microsoft Word' documents.
    You can reuse table styles, list styles but also add column sections,
    landscape oriented pages. Table and image captions as well as
    cross-references are transformed into 'Microsoft Word' fields,
    allowing documents edition and merging without issue with references;
    the syntax conforms to the 'bookdown' cross-reference definition.
    Objects generated by the 'officer' package are also supported in the
    'knitr' chunks.  'Microsoft PowerPoint' presentations also benefit
    from this as well as the ability to produce editable vector graphics
    in 'PowerPoint' and also to define placeholder where content is to be
    added.

Access and manipulate 'Microsoft Word', 'RTF' and 'Microsoft
    PowerPoint' documents from R.  The package focuses on tabular and
    graphical reporting from R; it also provides two functions that let
    users get document content into data objects. A set of functions lets
    add and remove images, tables and paragraphs of text in new or
    existing documents.  The package does not require any installation of
    Microsoft products to be able to write Microsoft files.

Detect the number and locations of change points. The locations can be either exact or in terms of ranges, 
            depending on the available computational resource. The method is based on Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh (2017) <doi:10.1109/TSP.2017.2711558>.