# Sequential Method for Classification and Generalized Estimating Equations Problem

## Documentation for package ‘seqest’ version 1.0.1

- DESCRIPTION file.

## Help Pages

ase_seq_logit | variable selection and stopping criterion
A_optimal_cat | Get the most informative subjects from unlabeled dataset for the categorical case
A_optimal_ord | Get the most informative subjects from unlabeled dataset for the ordinal case
D_optimal | Get the most informative subjects for the clustered data
evaluateGEEModel | The adaptive shrinkage estimate for generalized estimating equations
genBin | Generate the correlated binary response data for discrete case
genCorMat | Generate the correlation matrix for the clusteded data
gen_bin_data | generate the data used for the model experiment
gen_GEE_data | Generate the datasets with clusters
gen_multi_data | Generate the training data and testing data for the categorical and ordinal case.
getMH | Get the matrices M and H for the clustered data for the GEE case
getWH | Get the matrices W and H for the categorical case
getWH_ord | Get the matrices W and H for the ordinal case
init_multi_data | Generate the labeled and unlabeled datasets
is_stop_ASE | Determining whether to stop choosing sample
logit_model | the individualized binary logistic regression for categorical response data.
logit_model_ord | the individualized binary logistic regression for ordinal response data.
print.seqbin | Print the results by the binary logistic regression model
print.seqGEE | Print the results by the generalized estimating equations.
print.seqmulti | Print the results by the multi-logistic regression model
QIC | Calculate quasi-likelihood under the independence model criterion (QIC) for Generalized Estimating Equations.
seq_bin_model | The sequential logistic regression model for binary classification problem.
seq_cat_model | The sequential logistic regression model for multi-classification problem under the categorical case.
seq_GEE_model | The The sequential method for generalized estimating equations case.
seq_ord_model | The sequential logistic regression model for multi-classification problem under the ordinal case.
update_data_cat | Add the new sample into labeled dataset from unlabeled dataset for the categorical case
update_data_ord | Add the new sample into labeled dataset from unlabeled dataset for the ordinal case