The package has reached a significant milestone of maturity and stability, leading to the version update to 1.0.0.
Improvement: enhanced print methods.
Improvement: updated documentation.
Improvement: minor internal enhancements.
Improvement: multinomial_naive_bayes(), bernoulli_naive_bayes(), poisson_naive_bayes() and gaussian_naive_bayes() now support sparse matrices (dgCMatrix class from the Matrix Package).
Improvement: updated documentation.
Improvement: better informative errors.
Enhanced documentation - this includes a new webpage: https://majkamichal.github.io/naivebayes/
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.
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.
predict.naive_bayes() is now more efficient and more reliable.
print() method has been enhanced for better readability.
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".
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.
gaussian_naive_bayes() - specialised version of the naive_bayes(), where all features are real valued and each feature is modelled with the Gaussian distribution.
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.
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).
multinomial_naive_bayes() - specialised Naive Bayes classifier suitable for text classification.
%class% and %prob% - infix operators that are shorthands for performing classification and obtaining posterior probabilities, respectively.
coef() - a generic function which extracts model coefficients from specialized Naive Bayes objects.
get_cond_dist() - for obtaining names of class conditional distributions assigned to features.
Fixed: when laplace > 0 and discrete feature with >2 distinct values, the probabilities in the probability table do not sum up to 1.
Fixed: plot crashes when missing data present in training set (bug found by Mark van der Loo).
Fixed: numerical underflow in predict.naive_bayes function when the number of features is big (bug found by William Townes).
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.
Improvement: better handling (informative warnings/errors) of not correct inputs in 'predict.naive_bayes' function.
Improvement: print.naive_bayes fits now the console width.
Fixed: when the data have two classes and they are not alphabetically ordered, the predicted classes are incorrect (bug found by Max Kuhn).
Fixed: when the prediction data has one row, the column names get dropped (bug found by Max Kuhn).