
Mplus
 Referenced in 286 articles
[sw06511]
 observed or unobserved heterogeneity, and data that contain missing values. Analyses can be carried ... capabilities for Monte Carlo simulation studies, where data can be generated and analyzed according...

impute
 Referenced in 69 articles
[sw14376]
 means clustering are not robust to missing data, and may lose effectiveness even with ... missing values. Methods for imputing missing data are needed, therefore, to minimize the effect ... investigate automated methods for estimating missing data. Results: We present a comparative study of several ... imputation methods to the amount of missing data over the range of 1–20% missing...

Amelia
 Referenced in 22 articles
[sw04533]
 Amelia: Amelia II: A Program for Missing Data , Amelia II ”multiply imputes” missing data ... from a timeseriescrosssectional data set (such as collected by years for each ... have about the values of missing cells in their data. Amelia II also includes useful...

XGobi
 Referenced in 26 articles
[sw11752]
 interactive projection pursuit. Views of the data can be reshaped. Points can be labeled ... labeling, brushing, and sharing of projections. Missing data are accommodated and their patterns...

LogXact
 Referenced in 12 articles
[sw12105]
 coupled with small sample sizes or missing data sets. LogXact aims to provide simple ... continuous and binary, polytonomous, count, and missing data. Users of the software can be confident...

Mx
 Referenced in 13 articles
[sw19939]
 other types of statistical modeling of data. It offers the fitting fuctions found in commercial ... maximum likelihood estimation of parameters from missing data structures, under normal theory. Complex ’nonstandard’ models...

E4
 Referenced in 12 articles
[sw17878]
 framework that allows for measurement errors, missing data, vector GARCH errors and constraints...

Netica
 Referenced in 11 articles
[sw13961]
 ordinary tabdelimited form and have ”missing data...

StatMatch
 Referenced in 18 articles
[sw11933]
 also be used to impute missing values in data sets through hot deck imputation methods...

simFrame
 Referenced in 9 articles
[sw08021]
 guidelines regarding, e.g., simulation designs, contamination, missing data models or evaluation criteria are necessary...

Monomvn
 Referenced in 6 articles
[sw08173]
 arbitrary dimension where the pattern of missing data is monotone. Through the use of parsimonious/shrinkage ... handle a nearly arbitrary amount of missing data. The current version supports maximum likelihood inference...

VIM
 Referenced in 9 articles
[sw06776]
 used for exploring the data and the structure of the missing and/or imputed values. Depending ... missings and allows to explore the data including missing values. In addition, the quality...

PDB2PQR
 Referenced in 30 articles
[sw13087]
 such methods requires accurate and complete structural data as well as force field parameters such ... addition of missing atomic coordinates to molecular structures from the Protein Data Bank ... calculations, including adding a limited number of missing heavy atoms to biomolecular structures, estimating titration...

bernor
 Referenced in 4 articles
[sw19665]
 Monte Carlo likelihood inference for missing data models. We describe a Monte Carlo method ... likelihood estimate (MLE), when there are missing data and the observed data likelihood ... closed form. This method uses simulated missing data that are independent and identically distributed...

mhsmm
 Referenced in 6 articles
[sw19752]
 time series data, with multivariate and/or missing data. Allows user defined emission distributions...

BradleyTerry2
 Referenced in 11 articles
[sw09554]
 probit or cauchit models to paircomparison data. Included are the standard ‘unstructured’ BradleyTerry ... simple and efficient approach to handling missing covariate data, and suitablydefined residuals for diagnostic...

TASSEL
 Referenced in 5 articles
[sw11446]
 phenotypic and genotypic data, imputing missing data and calculating principal components. Availability: The TASSEL executable...

R/qtl
 Referenced in 10 articles
[sw20451]
 model (HMM) technology for dealing with missing genotype data. We have implemented the main...

ice
 Referenced in 4 articles
[sw24700]
 Chained Equations (MICE): Implementation in Stata. Missing data are a common occurrence in real datasets ... standard route to estimating models with missing covariate data under a missingatrandom assumption ... MICE approach to multiple imputation. Real data from an observational study in ovarian cancer...

bartMachine
 Referenced in 5 articles
[sw10962]
 package introduces many new features for data analysis using BART such as variable selection, interaction ... detection, model diagnostic plots, incorporation of missing data and the ability to save trees...