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center_data()
|
Centers the observations in a matrix by their respective class sample means |
|
cov_autocorrelation()
|
Generates a \(p \times p\) autocorrelated covariance matrix |
|
cov_block_autocorrelation()
|
Generates a \(p \times p\) block-diagonal covariance matrix with
autocorrelated blocks. |
|
cov_eigen()
|
Computes the eigenvalue decomposition of the maximum likelihood estimators
(MLE) of the covariance matrices for the given data matrix |
|
cov_intraclass()
|
Generates a \(p \times p\) intraclass covariance matrix |
|
cov_list()
|
Computes the covariance-matrix maximum likelihood estimators for each class
and returns a list. |
|
cov_mle()
|
Computes the maximum likelihood estimator for the sample covariance matrix
under the assumption of multivariate normality. |
|
cov_pool()
|
Computes the pooled maximum likelihood estimator (MLE) for the common
covariance matrix |
|
cov_shrink_diag()
|
Computes a shrunken version of the maximum likelihood estimator for the
sample covariance matrix under the assumption of multivariate normality. |
|
cv_partition()
|
Randomly partitions data for cross-validation. |
|
diag_estimates()
|
Computes estimates and ancillary information for diagonal classifiers |
|
dmvnorm_diag()
|
Computes multivariate normal density with a diagonal covariance matrix |
|
generate_blockdiag()
|
Generates data from K multivariate normal data populations, where each
population (class) has a covariance matrix consisting of block-diagonal
autocorrelation matrices. |
|
generate_intraclass()
|
Generates data from K multivariate normal data populations, where each
population (class) has an intraclass covariance matrix. |
|
h()
|
Bias correction function from Pang et al. (2009). |
|
lda_diag() predict(<lda_diag>)
|
Diagonal Linear Discriminant Analysis (DLDA) |
|
lda_eigen() predict(<lda_eigen>)
|
The Minimum Distance Rule using Moore-Penrose Inverse (MDMP) classifier |
|
lda_emp_bayes() predict(<lda_emp_bayes>)
|
The Minimum Distance Empirical Bayesian Estimator (MDEB) classifier |
|
lda_emp_bayes_eigen() predict(<lda_emp_bayes_eigen>)
|
The Minimum Distance Rule using Modified Empirical Bayes (MDMEB) classifier |
|
lda_pseudo() predict(<lda_pseudo>)
|
Linear Discriminant Analysis (LDA) with the Moore-Penrose Pseudo-Inverse |
|
lda_schafer() predict(<lda_schafer>)
|
Linear Discriminant Analysis using the Schafer-Strimmer Covariance Matrix
Estimator |
|
lda_shrink_cov() predict(<lda_shrink_cov>)
|
Shrinkage-based Diagonal Linear Discriminant Analysis (SDLDA) |
|
lda_shrink_mean() predict(<lda_shrink_mean>)
|
Shrinkage-mean-based Diagonal Linear Discriminant Analysis (SmDLDA) from
Tong, Chen, and Zhao (2012) |
|
lda_thomaz() predict(<lda_thomaz>)
|
Linear Discriminant Analysis using the Thomaz-Kitani-Gillies Covariance
Matrix Estimator |
|
log_determinant()
|
Computes the log determinant of a matrix. |
|
no_intercept()
|
Removes the intercept term from a formula if it is included |
|
plot(<rda_high_dim_cv>)
|
Plots a heatmap of cross-validation error grid for a HDRDA classifier object. |
|
posterior_probs()
|
Computes posterior probabilities via Bayes Theorem under normality |
|
qda_diag() predict(<qda_diag>)
|
Diagonal Quadratic Discriminant Analysis (DQDA) |
|
qda_shrink_cov() predict(<qda_shrink_cov>)
|
Shrinkage-based Diagonal Quadratic Discriminant Analysis (SDQDA) |
|
qda_shrink_mean() predict(<qda_shrink_mean>)
|
Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis (SmDQDA) from
Tong, Chen, and Zhao (2012) |
|
quadform()
|
Quadratic form of a matrix and a vector |
|
quadform_inv()
|
Quadratic Form of the inverse of a matrix and a vector |
|
rda_cov()
|
Calculates the RDA covariance-matrix estimators for each class |
|
rda_high_dim() predict(<rda_high_dim>)
|
High-Dimensional Regularized Discriminant Analysis (HDRDA) |
|
rda_high_dim_cv()
|
Helper function to optimize the HDRDA classifier via cross-validation |
|
rda_weights()
|
Computes the observation weights for each class for the HDRDA classifier |
|
regdiscrim_estimates()
|
Computes estimates and ancillary information for regularized discriminant
classifiers |
|
risk_stein()
|
Stein Risk function from Pang et al. (2009). |
|
solve_chol()
|
Computes the inverse of a symmetric, positive-definite matrix using the
Cholesky decomposition |
|
tong_mean_shrinkage()
|
Tong et al. (2012)'s Lindley-type Shrunken Mean Estimator |
|
two_class_sim_data
|
Example bivariate classification data from caret |
|
update_rda_high_dim()
|
Helper function to update tuning parameters for the HDRDA classifier |
|
var_shrinkage()
|
Shrinkage-based estimator of variances for each feature from Pang et al.
(2009). |