entropy
(Package: infotheo) :
entropy computation
entropy takes the dataset as input and computes the entropy according to the entropy estimator method.
● Data Source:
CranContrib
● Keywords: misc
● Alias: entropy
●
0 images
|
multiinformation takes a dataset as input and computes the multiinformation (also called total correlation) among the random variables in the dataset. The value is returned in nats using the entropy estimator estimator.
● Data Source:
CranContrib
● Keywords: misc
● Alias: multiinformation
●
0 images
|
mutinformation
(Package: infotheo) :
mutual information computation
mutinformation takes two random variables as input and computes the mutual information in nats according to the entropy estimator method. If Y is not supplied and X is a matrix-like argument, the function returns a matrix of mutual information between all pairs of variables in the dataset X.
● Data Source:
CranContrib
● Keywords: misc
● Alias: mutinformation
●
0 images
|
natstobits
(Package: infotheo) :
convert nats into bits
natstobits takes a value in nats (a double) as input and returns the value in bits (a double).
● Data Source:
CranContrib
● Keywords: misc
● Alias: natstobits
●
0 images
|
discretize
(Package: infotheo) :
Unsupervized Data Discretization
discretize discretizes data using the equal frequencies or equal width binning algorithm. "equalwidth" and "equalfreq" discretizes each random variable (each column) of the data into nbins. "globalequalwidth" discretizes the range of the random vector data into nbins.
● Data Source:
CranContrib
● Keywords: misc
● Alias: discretize
●
0 images
|
interinformation
(Package: infotheo) :
interaction information computation
interinformation takes a dataset as input and computes the the interaction information among the random variables in the dataset using the entropy estimator method. This measure is also called synergy or complementarity.
● Data Source:
CranContrib
● Keywords: misc
● Alias: interinformation
●
0 images
|
condentropy
(Package: infotheo) :
conditional entropy computation
condentropy takes two random vectors, X and Y, as input and returns the conditional entropy, H(X|Y), in nats (base e), according to the entropy estimator method. If Y is not supplied the function returns the entropy of X - see entropy.
● Data Source:
CranContrib
● Keywords: misc
● Alias: condentropy
●
0 images
|
condinformation
(Package: infotheo) :
conditional mutual information computation
condinformation takes three random variables as input and computes the conditional mutual information in nats according to the entropy estimator method. If S is not supplied the function returns the mutual information between X and Y - see mutinformation
● Data Source:
CranContrib
● Keywords: misc
● Alias: condinformation
●
0 images
|
infotheo
(Package: infotheo) :
Information Theory package
The package infotheo provide various estimators for computing information-theoretic measures from data
● Data Source:
CranContrib
● Keywords: misc
● Alias: infotheo
●
0 images
|
providing 
.