A chi-square inverse cumulative distribution function |
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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 |
Build models and perform predictions using the elastic net regression method |
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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 lasso() |
Build models and perform predictions using the Lasso regression method |
Perform cumulative sum on a matrix or a multidimensional matrix |
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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 marix |
Ø mnorm() |
Normalize a matrix or a multidimensional matrix |
Ø mstd() |
Calculate the standard deviation on a matrix or a multidimensional marix |
Ø msum() |
Calculate sum on a matrix or a multidimensional marix |
An inverse normal distribution function |
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Ø ones() |
Create a multidimensional matrix where all the elements are1 |
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 sg() |
Perform SG smoothing on each row of a vector or a matrix |
Ø tinv() |
T inverse cumulative distribution function |
Ø zeros() |
Create a multidimensional matrix where all the elements are zero |