Math

Ø  A.bi()

Split a low-frequency categorical enumerated sequence variable that contains a number of categories not greater than 6 into multiple binary variables during modeling

Ø  A.corskew()

Correct skewness of a numeric sequence

Ø  A.datederive()

Generate multiple derivative variables from a datetime sequence variable

Ø  A.dateinterval()

Generate multiple date difference variables for a datetime sequence/table sequence variable

Ø  A.impute()

Impute missing values to a sequence type variable during modeling

Ø  A.mi()

Create indicator variable for missing values in a sequence variable

Ø  A.mvp()

Create indicator variables for the MVP analysis and automatically perform the subsequent handling according to a sequence of indicator variables for missing values

Ø  A.numnorm()

Normalize a sequence type numeric variable during modeling

Ø  A.range()

Divide a sequence into a specified number of equal segments according to its length and returns the k^th segment

Ø  A.sert()

Remove outliers from a sequence type numeric variable during modeling

Ø  A.setenum()

Map enumerated values as integers

Ø  A.smooth()

Perform smoothing on a sequence type variable during modeling

Ø  A.tarcorskew()

Correct skewness of a sequence of numeric target variables

Ø  chi_p()

Calculate p-value of chi-square test

Ø  comabs()

Calculate the modulus of a sequence of complex numbers

Ø  comangle()

Calculate angles of complex numbers

Ø  comconj()

Calculate a complex number’s complex conjugate

Ø  comexp()

Create complex exponentials

Ø  comimage()

Get a complex number’s imaginary part

Ø  compair()

Sort complex numbers in the form of complex conjugate pairs

Ø  complex()

Create a sequence of complex numbers

Ø  comreal()

Get a complex number’s real part

Ø  comsign()

A sign function that only handles complex number

Ø  comstr()

Output complex numbers as strings in the form of a+bi

Ø  comunwrap()

Shift phase angles

Ø  chi2inv()

A chi-square inverse cumulative distribution function

Ø  cov()

Calculate the covariance between two vectors

Ø  covm()

Calculate the covariance matrix for a matrix

Ø  dism()

Calculate Mahalanobis distance between two vectors on covariance matrix

Ø  elasticnet()

Build models and perform predictions using the elastic net regression method

Ø  eye()

Create a matrix whose major diagonal element is 1 and other elements are 0

Ø  finv()

An inverse cumulative distribution function F

Ø  fisher_p()

Calculate p-value for Fisher’s exact test

Ø  freq()

Calculate the frequency of a specified member in a sequence

Ø  kmeans()

Perform an unsupervised clustering algorithm that divides a dataset into predetermined number of clusters based on the minimum error function

Ø  lasso()

Build models and perform predictions using the Lasso regression method

Ø  lineplan()

For linear programming and calculate minimum value in linear objective function under linear constraints

Ø  mcumsum()

Perform cumulative sum on a matrix or a multidimensional matrix

Ø  mfind()

Search for positions of non-zero members in a vector or matrix

Ø  mmean()

Calculate the mean value within a matrix or a multidimensional matrix 

Ø  mnorm()

Normalize a matrix or a multidimensional matrix

Ø  mstd()

Calculate the standard deviation on a matrix or a multidimensional matrix

Ø  msum()

Calculate sum on a matrix or a multidimensional matrix

Ø  norminv()

An inverse normal distribution function

Ø  ones()

Create a multidimensional matrix where all the elements are1

Ø  P.bi()

Split a low-frequency categorical enumerated table sequence/record sequence variable that contains a number of categories not greater than 6 into multiple binary variables during modeling

Ø  P.corskew()

Correct skewness of a numeric variable

Ø  P.datederive()

Generate multiple derivative variables from a date table sequence/record sequence variable

Ø  P.dateinterval()

Generate multiple date difference variables for a datetime table sequence/record sequence variable

Ø  P.impute()

Impute missing values to a table sequence/record sequence type variable during modeling

Ø  P.mi()

Create indicator variable for missing values for a table sequence/record sequence variable during modeling

Ø  P.mvp()

Create indicator variables for the MVP analysis and automatically perform the subsequent handling according to a table sequence/record sequence of indicator variables for missing values

Ø  P.numnorm()

Normalize a table sequence/record sequence numeric variable during modeling

Ø  P.sert()

Remove outliers from a table sequence/record sequence numeric variable during modeling

Ø  P.setenum()

Map enumerated variable values as integers

Ø  P.smooth()

Perform smoothing on a table sequence/record sequence variable of a table sequence/ record sequence during modeling

Ø  P.tarcorskew()

Correct skewness of a table sequence/record sequence numeric variable

Ø  pca()

Perform PCA on a matrix and return data for dimensionality reduction

Ø  pls()

Fit together matrices using PLS technique

Ø  ridge()

Build models and perform predictions using the ridge regression method

Ø  se()

Calculate the standard error of a numeric sequence

Ø  sg()

Perform SG smoothing on each row of a vector or a matrix

Ø  skew()

Calculate the skewness of a sequence of numeric data

Ø  svm()

Solve binary classification problems and regression problems by supporting vector machines

Ø  tinv()

T inverse cumulative distribution function

Ø  ttest_p()

Calculate t-test’s p-value

Ø  zeros()

Create a multidimensional matrix where all the elements are zero