Changes in version 1.0.0 (2024-03-16)                  

Major Release: Maturity and Stability:

    o   The package has reached a significant milestone of maturity and
	stability, leading to the version update to 1.0.0.

    o   Improvement: enhanced print methods.

    o   Improvement: updated documentation.

    o   Improvement: minor internal enhancements.

                 Changes in version 0.9.7 (2020-03-08)                  

    o   Improvement: multinomial_naive_bayes(),
	bernoulli_naive_bayes(), poisson_naive_bayes() and
	gaussian_naive_bayes() now support sparse matrices (dgCMatrix
	class from the Matrix Package).

    o   Improvement: updated documentation.

    o   Improvement: better informative errors.

                 Changes in version 0.9.6 (2019-06-03)                  

Improvements:

    o   Enhanced documentation - this includes a new webpage:
	https://majkamichal.github.io/naivebayes/

    o   naive_bayes(): Poisson distribution is now available to model
	class conditional probabilities of non-negative integer
	predictors. It is applied to all vectors with class "integer"
	via a new parameter usepoisson = TRUE in naive_bayes function.
	By default usepoisson = FALSE. All naive_bayes objects created
	with previous versions are fully compatible with the 0.9.6
	version.

    o   predict.naive_bayes() has new parameter eps that specifies a
	value of an epsilon-range to replace zero or close to zero
	probabilities by specified threshold. It applies to metric
	variables.

    o   predict.naive_bayes() is now more efficient and more reliable.

    o   print() method has been enhanced for better readability.

    o   plot() method allows now visualising class marginal and class
	conditional distributions for each predictor variable via new
	parameter prob with two possible values: "marginal" or
	"conditional".

New functions:

    o   bernoulli_naive_bayes() - specialised version of the
	naive_bayes(), where all features take on 0-1 values and each
	feature is modelled with the Bernoulli distribution.

    o   gaussian_naive_bayes() - specialised version of the
	naive_bayes(), where all features are real valued and each
	feature is modelled with the Gaussian distribution.

    o   poisson_naive_bayes() - specialised version of the
	naive_bayes(), where all features take are non-negative
	integers and each feature is modelled with the Poisson
	distribution.

    o   nonparametric_naive_bayes() - specialised version of the
	naive_bayes(), where all features take real valued and
	distribution of each is estimated with kernel density
	estimation (KDE).

    o   multinomial_naive_bayes() - specialised Naive Bayes classifier
	suitable for text classification.

    o   %class% and %prob% - infix operators that are shorthands for
	performing classification and obtaining posterior
	probabilities, respectively.

    o   coef() - a generic function which extracts model coefficients
	from specialized Naive Bayes objects.

    o   get_cond_dist() - for obtaining names of class conditional
	distributions assigned to features.

                 Changes in version 0.9.5 (2019-03-17)                  

    o   Fixed: when laplace > 0 and discrete feature with >2 distinct
	values, the probabilities in the probability table do not sum
	up to 1.

                 Changes in version 0.9.4 (2019-03-10)                  

    o   Fixed: plot crashes when missing data present in training set
	(bug found by Mark van der Loo).

                 Changes in version 0.9.3 (2019-01-07)                  

    o   Fixed: numerical underflow in predict.naive_bayes function when
	the number of features is big (bug found by William Townes).

    o   Fixed: when all names of features in the newdata in
	predict.naive_bayes function do not match these defined in the
	naive_bayes object, then the calculation based on prior
	probabilities is done only for one row of newdata.

    o   Improvement: better handling (informative warnings/errors) of
	not correct inputs in 'predict.naive_bayes' function.

    o   Improvement: print.naive_bayes fits now the console width.

                 Changes in version 0.9.2 (2018-01-03)                  

    o   Fixed: when the data have two classes and they are not
	alphabetically ordered, the predicted classes are incorrect
	(bug found by Max Kuhn).

                 Changes in version 0.9.1 (2017-01-15)                  

    o   Fixed: when the prediction data has one row, the column names
	get dropped (bug found by Max Kuhn).