NEWS.md
R CMD CHECK. #38Fixed an issue with the HDRDA classifier’s predict function. The posterior probabilities did not sum to 1 because they were unnormalized. #34
Fixed another issue with the HDRDA classifier’s predict function, where the class names were incorrect when predicting a single observation. #34
Improved docs throughout the package to pass R CMD CHECK. #35
The predict function now returns posterior-probability estimates for each classifier.
The object returned by cv_hdrda() can be plotted. A heatmap is produced using ggplot2 to illustrate the cross-validation error rate for each tuning-parameter pair considered.
The predict function for the HDRDA classifier is now substantially faster when classifying a large number of observations. #33
The cross-validation helper function cv_hdrda() for the HDRDA classifier now returns a trained classifier rather than the optimal model.
cv_hdrda() also has an optional verbose argument to dump summary information while the cross-validation is running.
Fixed issue with classifiers’ documentation not appearing in help index. #26
Better handling of HDRDA when its tuning parameters are both 0.
Corrected calculation of W_k and Q_k in HDRDA classifier.
Added unit tests for HDRDA.
Can now specify population means in generate_blockdiag().
Added unit tests for generate_blockdiag().
Updated man docs with roxygen2 4.0.
Added log_determinant() helper function to calculate the log-determinant of a matrix.
The High-Dimensional Regularized Discriminant Analysis (HDRDA) classifier from Ramey, Stein, and Young (2014) implemented in hdrda() has been revamped to improve its computational performance.
lda_pseudo() is an implementation of Linear Discriminant Analysis (LDA) with the Moore-Penrose Pseudo-Inverse
lda_schafer() is an implementation of Linear Discriminant Analysis (LDA) using the covariance matrix estimator from Schafer and Strimmer (2005)
lda_thomaz() is an implementation of Linear Discriminant Analysis (LDA) using the covariance matrix estimator from Thomaz, Kitani, and Gillies (2006)
mdeb() is an implementation of the Minimum Distance Empirical Bayesian Estimator (MDEB) classifier from Srivistava and Kubokawa (2007)
mdmeb() is an implementation of the Minimum Distance Rule using Modified Empirical Bayes (MDMEB) classifier from Srivistava and Kubokawa (2007)
mdmp() is an implementation of the Minimum Distance Rule using Moore-Penrose Inverse (MDMP) classifier from Srivistava and Kubokawa (2007)
smdlda() is an implementation of the Shrinkage-mean-based Diagonal Linear Discriminant Analysis (SmDLDA) from Tong, Chen, and Zhao (2012)
smdqda() is an implementation of the Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis (SmDQDA) from Tong, Chen, and Zhao (2012)
Added a summary function for hdrda classifiers
First version of the sparsediscrim package. With this package, we aim to provide a large collection of regularized and sparse discriminant analysis classifiers intended for high-dimensional classification.
hdrda() is an implementation of the High-Dimensional Regularized Discriminant Analysis classifier from Ramey, Stein, and Young (2014).
dlda() is an implementation of the Diagonal Linear Discriminant Analysis classifier from Dudoit, Fridlyand, and Speed (2002).
dqda() is an implementation of the Diagonal Quadratic Discriminant Analysis classifier from Dudoit, Fridlyand, and Speed (2002).
sdlda() is an implementation of the Shrinkage-based Diagonal Linear Discriminant Analysis classifier from Pang, Tong, and Zhao (2009).
sdqda() is an implementation of the Shrinkage-based Diagonal Quadratic Discriminant Analysis classifier from Pang, Tong, and Zhao (2009).
generate_blockdiag() generates random variates from K multivariate normal populations, where each class is generated with a constant mean vector and a covariance matrix consisting of block-diagonal autocorrelation matrices.
generate_intraclass() generates random variates from K multivariate normal populations, where class is generated with a constant mean vector and an intraclass covariance matrix.
cv_partition() randomly partitions data for cross-validation.
no_intercept() removes the intercept term from a formula if it is included.
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 for the common covariance matrix under the assumption of multivariate normality.
cov_eigen() computes the eigenvalue decomposition of the maximum likelihood estimators of the covariance matrices for the given data matrix. We provide an option to calculate the eigenvalue decomposition using the Fast Singular Value Decomposition, which can greatly expedite the eigenvalue decomposition for very tall data (large n, small p) or very wide data (small n, large p).