Title: Longest Increasing Subsequence Independence Test
Author: Jesus Garcia and Veronica Andrea Gonzalez Lopez
Description: calculate the pvalue for the two samples L.I.S
independence test (for continuous random variables).

Title: Tests of independence based on the Longest Increasing
Author: J. E. Garcia and V. A. Gonzalez-Lopez
Maintainer: J. E. Garcia <jg@ime.unicamp.br>
Description: Tests for independence between X and Y computed from a paired sample (x1,y1),...(xn,yn) of (X,Y), using one of the following statistics (a) the Longest Increasing Subsequence (Ln), (b) JLn, a Jackknife version of Ln or (c) JLMn, a Jackknife version of the longest monotonic subsequence. This family of tests can be applied under the assumption of continuity of X and Y.

Description: Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. Please see Pavlik, Eglington, and Harrel-Williams (2021) <https://ieeexplore.ieee.org/document/9616435>. 'LKT' is a method to compute features of student data that are used as predictors of subsequent performance. 'LKT' allows great flexibility in the choice of predictive components and features computed for these predictive components. The system is built on top of 'LiblineaR', which enables extremely fast solutions compared to base glm() in R.
Depends: R (>= 3.5.0), SparseM (>= 1.78), Matrix, methods, data.table
Suggests: rmarkdown, pROC (>= 1.16.2), knitr, caret, utils
Author: Philip I. Pavlik Jr. [aut, ctb, cre]
Luke G. Eglington [aut, ctb] (<https://orcid.org/0000-0002-8432-9203>)
Maintainer: Philip I. Pavlik Jr. <imrryr@gmail.com>

Description: Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. Please see Pavlik, Eglington, and Harrel-Williams (2021) <arXiv:2005.00869>. 'LKT' is a method to compute features of student data that are used as predictors of subsequent performance. 'LKT' allows great flexibility in the choice of predictive components and features computed for these predictive components. The system is built on top of 'LiblineaR', which enables extremely fast solutions compared to base glm() in R.
Matrix, methods, knitr, data.table (>= 1.13.2), LiblineaR (>=