analyticlm | Analytic solution for least squares |
analyticridge | Functions associated with ridge regression Analytic solution... |
argmax | Given a matrix, find the max row value position |
bayes | Bayesian object |
bernoulli_sim | Simulate Bernoulli distribution from HW3 |
bgd | Gradient descent algorithms |
binomcdf | Compute the binomial cumulative density function |
binom_mean | Mean of binomial variable |
binompdf | Compute the binomial probability density function |
binom_sd | Standard deviation of binomial variable |
categoricalProbs.bayes | Conditional probability fitting step for Categorical data |
combs | Discrete random variables - binomial probability Compute the... |
confusionMatrix.bayes | Confusion matrix for reporting accuracy |
continuousProbs.bayes | Conditional probability fitting step for Continuous data |
coord_cut | Utility functions for working with Bayesian classifier Create... |
create_outliers | Create outliers for 1-d vector X from hw1 specs |
create_splits | For use with in generating training and test sets Create... |
data_simulator | Simulate data for hw |
fit.bayes | Fit training data to Bayesian Classifier |
gauss_kern | Kernels used for localized estimation Gaussian Kernel |
geometcdf | Geometric cumulative density function |
geometpdf | Geometric probability density function for first success in x... |
gridSearch.bayes | Implement grid search for the bayes object |
grp_covar | Return a p*p covariance matrix for class K |
grp_indices | Implementations related to Discriminant analysis Create... |
grp_mean | Mean of each predictor attribute for each class K |
huber_cond | Conditional for Huber Loss function |
huber_loss | Huber Loss function (smooth mean absolute error) with... |
kblock_kern | K-Block Kernel |
kernelDensity.bayes | Kernel Density Estimate for Naive Bayes |
lasso_gradient | Functions associated with lasso regression |
ldf | Linear discriminant function (LDF) implementation |
least_squares | Least squares objective |
least_squares_gradient | Least squares gradient |
least_squares_huber_gradient | Least Squares with Huber Loss Function gradient |
least_squares_l1_gradient | Least Squares with Mean Absolute Error (L1 norm) gradient |
least_squares_ql_gradient | Least Squares with Quadratic Loss gradient |
lgm_gradient | Logistic gradient computation |
lgm_yhat | Implementations for Logistic Regression Compute yhat for... |
linda | Linear Discriminant Analysis implementation |
local_lm | Local Linear Regression |
logistic_gradient | Compute the logistic gradient with learning rate alpha |
LOGMIN | Implement Naive Bayes Classifier |
lsg | Least squares gradient with different handling of alpha |
mae | Compute Mean Absolute Error (L1 norm) |
matrix_group | Convert matrix to visualization format |
mtcars | Testing data documentation mtcars dataset |
normalize | Normalize a numeric object |
nsim | Simulate a random normal distribution on an interval |
pi_k | Compute prior probabilities for each class K |
plotgrid | Coordinate grid for plotting |
pooled_covar | Compute Pooled Covariance Matrix |
popmat | Populate a Spatial Matrix with Aggregated Values |
postSpatialProbs.bayes | Posterior Spatial probabilties |
predict.bayes | Predict log probabilities for each class K |
predict_proba.bayes | Predicts the log probabilities for each class and feature |
priorProbs.bayes | Prior probability fitting step for Continous/Categorical data |
quadratic_loss | Loss functions from hw1 - related to linear regression... |
score.bayes | Accuracy scoring function for Binomial Naive Bayes |
sgd | Stochastic Gradient Descent |
spatial_fit.bayes | Fit spatial data |
spatialProbs.bayes | Conditional probability fitting step for Spatial data |
w | mtcars derivative variables |
wls | Implement local linear regression with a Gaussian kernel, as... |
x | mtcars derivative variables |
y | mtcars derivative variables |
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