Changes in version 1.0.0 (2023-09-19)                  

  - New decission_tree() function to create a perfect decision tree
    based on a set of observations and (if selected) see the
    step-by-step procedure;
  - New multivariate_linear_regression() function to generate linear
    equation lines that approximate the values on a set of observations
    and (if selected) see the step-by-step procedure;
  - New polynomial_regression() function to generate polynomial equation
    lines that approximate the values on a set of observations to
    selected degree and (if selected) see the step-by-step procedure;
  - New perceptron() function to calculate the weights of a perceptron
    and predict values on a set of observations and (if selected) see
    the step-by-step procedure;
  - New knn() function to perform k-nearest neighbors classification on
    a set of observations and (if selected) see the step-by-step
    procedure;
  - New print.tree_struct() function that prints a tree with the
    structure of the output of the decision_tree() function;
  - New act_method() function that calculates the selected activation
    function to a given input;
  - New db1rl data.frame with 20 observations (4 features). Values form
    different types of lines (linear, exponential, logarithmic, sine);
  - New db_per_and data.frame with 8 observations (2 features). "AND"
    logic gate;
  - New db_per_or data.frame with 8 observations (2 features). "OR"
    logic gate;
  - New db_per_xor data.frame with 8 observations (2 features). "XOR"
    logic gate;
  - New db_flowers data.frame with 25 observations (4 features)
    containing values about flowers;
  - New db2 data.frame with 10 observations (4 features) containing
    values about vehicles;
  - New db3 data.frame with 12 observations (5 features) containing
    values about vehicles.
  - New db_tree_struct data.frame with 12 observations (5 features)
    containing values about vehicles.

  - Initial CRAN submission.