Learn R Programming

cvam (version 0.9.3)

Coarsened Variable Modeling

Description

Extends R's implementation of categorical variables (factors) to handle coarsened observations; implements log-linear models for coarsened categorical data, including latent-class models. Detailed information and examples are provided in the package vignettes.

Copy Link

Version

Install

install.packages('cvam')

Monthly Downloads

23

Version

0.9.3

License

GPL-3 | file LICENSE

Maintainer

Joseph Schafer

Last Published

February 22nd, 2023

Functions in cvam (0.9.3)

cvamControl

Control Parameters for cvam
baseLevels

Get Coarsened Factor Attributes
cvam-package

tools:::Rd_package_title("cvam")
coarsened

Coarsened Factors
anova.cvam

Comparing the Fit of Two or More Models
cvamEstimate

Obtain Estimated Probabilities from a Fitted Model
cvam

Log-Linear Models for Incomplete Categorical Variables
crime

Crime Victimization Data
cig2019

Cigarette Use from the 2019 National Health Interview Survey
abortion2000

Abortion Attitudes from the 2000 General Social Survey
get.coef

Extract Information from a Coarsened-Variable Model
dropCoarseLevels

Remove Coarse Levels from a Coarsened Factor
latentFactor

Latent Factor
cvamLik

Likelihood of Observed Data Patterns
cvamPredict

Predict Missing or Coarsened Values from a Fitted Model
cvamImpute

Impute Data from a Fitted Model
miInference

Combine results from analyses after multiple imputation
is.naCoarsened

Missing-Value Indicators for Coarsened Factors
cvamPrior

Data-Augmentation Prior for Coarsened Factor Loglinear Model
hivtest

HIV test dataset
summary.cvam

Summarize a cvam Object
strokePatients

Stroke Patient Data
seatbelt

Seatbelt Data
microUCBAdmissions

UC Berkeley Graduate Admissions Microdata