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Title:	Sparse Latent Class Model for Cognitive Diagnosis
Version:	0.1.1
Description:	Perform a Bayesian estimation of the exploratory Sparse Latent Class Model for Binary Data described by Chen, Y., Culpepper, S. A., and Liang, F. (2020) <doi:10.1007/s11336-019-09693-2>.
License:	GPL-2 |GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://tmsalab.github.io/slcm/,https://github.com/tmsalab/slcm
BugReports:	https://github.com/tmsalab/slcm/issues
Depends:	R (≥ 4.3.0)
Imports:	Rcpp (≥ 1.1.0), edmdata
LinkingTo:	Rcpp, RcppArmadillo (≥ 15.0.2-2)
Suggests:	altdoc
Encoding:	UTF-8
RoxygenNote:	7.3.3
NeedsCompilation:	yes
Packaged:	2025-09-26 16:58:03 UTC; ronin
Author:	James Joseph Balamuta [aut, cre, cph], Steven Andrew Culpepper [aut, cph]
Maintainer:	James Joseph Balamuta <balamut2@illinois.edu>
Repository:	CRAN
Date/Publication:	2025-09-28 12:20:02 UTC

slcm: Sparse Latent Class Model for Cognitive Diagnosis

Description

Perform a Bayesian estimation of the exploratory Sparse Latent Class Model for Binary Data described by Chen, Y., Culpepper, S. A., and Liang, F. (2020)doi:10.1007/s11336-019-09693-2.

Author(s)

Maintainer: James Joseph Balamutabalamut2@illinois.edu (ORCID) [copyright holder]

Authors:

• Steven Andrew Culpeppersculpepp@illinois.edu (ORCID) [copyright holder]

See Also

Useful links:

• https://tmsalab.github.io/slcm/

• https://github.com/tmsalab/slcm

• Report bugs athttps://github.com/tmsalab/slcm/issues

Generate attribute pattern table header

Description

Generate attribute pattern table header

Usage

attribute_pattern_table_header(k, m = 2, order = k)

Arguments

Value

Return a matrix containing the class table

Examples

# K = 3attribute_pattern_table_header(3)# K = 4attribute_pattern_table_header(4)

Print the SLCM object

Description

Custom printing class to reveal features of the fitted SLCM.

Usage

## S3 method for class 'slcm'print(x, digits = max(3L, getOption("digits") - 3L), ...)

Arguments

Value

Print details and estimates found within the fitted SLCM.Return the model invisibly (viainvisible())

Sparse Latent Class Model for Cognitive Diagnosis (SLCM)

Description

Performs the Gibbs sampling routine for a sparse latent class modelas described in Chen et al. (2020) <doi: 10.1007/s11336-019-09693-2>

Usage

slcm(  y,  k,  burnin = 1000L,  chain_length = 10000L,  psi_invj = c(1, rep(2, 2^k - 1)),  m0 = 0,  bq = 1)

Arguments

Details

Theestimates list contains the mean information from the samplingprocedure. Meanwhile, thechain list contains full MCMC values. Lastly,thedetails list provides information regarding the estimation call.

Value

Anslcm object containing three named lists:

• estimates

  • beta: Average beta coefficients

  • theta: Average theta coefficients

  • delta: Average activeness of coefficients

  • class: Average class membership

  • pi: Average attribute class probability.

  • omega: Average omega

  • q: Average activeness of Q matrix entries based on heuristic transformation.

  • m2ll: Average negative two times log-likelihood

• chain

  • theta: theta coefficients iterations

  • beta: beta coefficients iterations

  • class: class membership iterations

  • pi: attribute class probability iterations

  • omega: omega iterations

  • m2ll: Negative two times log-likelihood iterations

• details

  • n: Number of Subjects

  • j: Number of Items

  • k: Number of Traits

  • l1: Slab parameter

  • m0,bq: Additional tuning parameters

  • burnin: Number of Iterations to discard

  • chain_length: Number of Iterations to keep

  • runtime: Duration of model run inside of the C++ code. (Does not include summarization of MCMC chain.)

  • package_version: Version of the package the SLCM model was fit with.

  • date_time: Date and Time the model was fit.

Examples

# Load Namespacenaming = requireNamespace("edmdata", quietly = TRUE)# Use a demo data set from the paperdata("items_matrix_reasoning", package = "edmdata")  burnin = 50        # Set for demonstration purposes, increase to at least 1,000 in practice.chain_length = 100 # Set for demonstration purposes, increase to at least 10,000 in practice.    model_reasoning = slcm(edmdata::items_matrix_reasoning, k = 4,                        burnin = burnin, chain_length = chain_length)                         print(model_reasoning)