Tests of independence based on the Longest Increasing
Subsequence
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.

c(person(given = "Philip I.",
family = "Pavlik Jr.",
role = c("aut","ctb","cre"),
email = "imrryr@gmail.com",
comment = c(ORCID = "0000-0001-6467-9452")),
person(given = "Luke G.",
family = "Eglington",
role = c("aut","ctb"),
email = "luke.eglington.mail@gmail.com",
comment = c(ORCID = "0000-0002-8432-9203"))
)
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.
rmarkdown, pROC (>= 1.16.2), knitr, caret, utils
Philip I. Pavlik Jr. [aut, ctb, cre]
(<https://orcid.org/0000-0001-6467-9452>),
Luke G. Eglington [aut, ctb] (<https://orcid.org/0000-0002-8432-9203>)

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.