ProcData-package: ProcData: A package for process data analysis
In ProcData: Process Data Analysis

Description Data structure Read sequences Sequence generators Feature extraction methods Sequence models Author(s) See Also

General tools for exploratory process data analysis. Process data refers to the data describing participants' problem solving processes in computer-based assessments. It is often recorded in computer log files. This package a process dataset and functions for reading processes from a csv file, process manipulation, action sequence generators. It also implements two automatic feature extraction methods that compress the information stored in process data, which often has a nonstandard format, into standard numerical vectors. This package also provides recurrent neural network based models that relate response processes with other binary or scale variables of interest. The functions that involve training and evaluating neural networks are based on functions in keras.

ProcData organizes response processes as an object of class proc. Some functions are provided for summarizing and manipulating proc objects.

summary.proc calculates summary statistics of a proc object.
remove_action removes actions and the corresponding timestamps
replace_action replaces an action by another action
combine_actions combines consecutive action into one action.

read.seqs reads response processes from a csv file.

seq_gen generates action sequences of an imaginery simulation-based item.
seq_gen2 generates action sequences according to a given probability transition matrix.
seq_gen3 generates action sequences according to a recurrent neural network.

seq2feature_mds extracts features from response processes by multidimensional scaling.
seq2feature_seq2seq extracts features from response processes by autoencoder.
seq2feature_ngram extracts ngram features from response processes.

seqm fits a neural network model that relates response processes with a response variable.
predict.seqm makes predictions from the models fitted by seqm.

Maintainer: Xueying Tang xueyingtang1989@gmail.com

Authors: