Package: CR2 0.2.1

Francis Huang

CR2: Compute Cluster Robust Standard Errors with Degrees of Freedom Adjustments

Estimate different types of cluster robust standard errors (CR0, CR1, CR2) with degrees of freedom adjustments. Standard errors are computed based on 'Liang and Zeger' (1986) <doi:10.1093/biomet/73.1.13> and Bell and 'McCaffrey' <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ>. Functions used in Huang and Li <doi:10.3758/s13428-021-01627-0>, Huang, 'Wiedermann', and 'Zhang' <doi:10.1080/00273171.2022.2077290>, and Huang, 'Zhang', and Li (forthcoming: Journal of Research on Educational Effectiveness).

Authors:Francis Huang [aut, cre], Bixi Zhang [aut]

# Install 'CR2' in R:

install.packages('CR2', repos = c('https://flh3.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

On CRAN:

Last updated 1 years agofrom:cf7e9f6818. Checks:3 OK, 6 NOTE. Indexed: yes.

Help page	Topics
Cluster robust standard errors with degrees of freedom adjustments (for lm and glm objects)	clustSE
Simulated data from 18 schools (from a cluster randomized controlled trial)	crct
Get V matrix for merMod objects	getV
Glance at goodness-of-fit statistics	glance.CR2
Grade point average (GPA) data of students from 25 schools	gpadat
Compute the inverse square root of a matrix	MatSqrtInverse
Testing for nonconstant variance (ncv)	ncvMLM
Cluster robust standard errors with degrees of freedom adjustments for lmerMod/lme objects	robust_mixed
Compute Satterthwaite degrees of freedom	satdf
Data from 25 schools (based on the NELS dataset)	sch25
Data from Project SHARE	sharedat
Tidy a CR2 object	tidy.CR2