feat(compassist): Edited README and new module to the package

README.md

@@ -19,7 +19,7 @@ The following functions are currently available:
 
 -   `dry_calc()`: Computes the probability of obtaining at least one of a specific outcome in a given number of trials based on binomial probability (i.e. probability of obtaining an item from a boss in a given number of kills). Displays a plot showing probability of obtaining a drop over a range of trial counts, indicating location of provided trials on this curve.
 
--   `pts_calc()`: Computes the expected play time to obtain a target point level (i.e. price of an item, expected number of points per item drop) as a function of a player's points per attempt and time per attempt. When passed multiple sets of points/ times, it ranks all of the possible strategies and provides a list of time required (in ranked order least to maximum).
+-   `pts_calc()`: Computes the expected play time to obtain a target point level (i.e. time required to achieve the target points) as a function of a player's points per attempt and time per attempt. When passed multiple sets of points/ times, it ranks all of the possible strategies and provides a list of time required (in ranked order least to maximum).
 
 There are some tools that perform similar functions to functions in `compassist`. For example, the `giovanni` package <https://github.com/tgsmith61591/giovanni> provides similar applications for hunting Shiny Pokemon. Users with sufficient understanding can also use mainstream statistical tools (i.e. `scipy` <https://github.com/scipy/scipy>) to replicate the basic functionalities of functions like `dry_calc`. The unique application of this package is to provide a centralized location for multiple different tools, to simplify calculation for users with less statistical understanding and tailor outputs to specific video game applications, as well as to provide additional helpful functionalities such as visualizations and rankings.