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:
Susu Zhang susu.zhang1992@gmail.com
Zhi Wang zhiwpku@gmail.com
Jingchen Liu jcliu@stat.columbia.edu
Zhiliang Ying zying@stat.columbia.edu
Useful links:
Report bugs at https://github.com/xytangtang/ProcData/issues
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