
Mplus
 Referenced in 270 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 61 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...

XGobi
 Referenced in 21 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...

Amelia
 Referenced in 11 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...

LogXact
 Referenced in 10 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...

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

Netica
 Referenced in 10 articles
[sw13961]
 ordinary tabdelimited form and have ”missing data...

Mx
 Referenced in 10 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...

StatMatch
 Referenced in 17 articles
[sw11933]
 also be used to impute missing values in data sets through hot deck imputation methods...

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...

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...

PDB2PQR
 Referenced in 25 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...

R/qtl
 Referenced in 9 articles
[sw20451]
 model (HMM) technology for dealing with missing genotype data. We have implemented the main...

TASSEL
 Referenced in 4 articles
[sw11446]
 phenotypic and genotypic data, imputing missing data and calculating principal components. Availability: The TASSEL executable...

bmem
 Referenced in 2 articles
[sw07439]
 Methods for mediation analysis with missing data Despite wide applications of both mediation models ... missing data techniques, formal discussion of mediation analysis with missing data is still rare ... compare four approaches to dealing with missing data in mediation analysis including listwise deletion, pairwise ... four methods for mediation analysis with missing data in the structural equation modeling framework...

BradleyTerry2
 Referenced in 7 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...

bartMachine
 Referenced in 4 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...

repolr
 Referenced in 4 articles
[sw07133]
 firstorder autoregressive correlation models, for data potentially recorded at irregularly spaced time intervals ... assumption of proportional odds and accommodates missing data, is described and applied to a clinical...

momentuHMM
 Referenced in 4 articles
[sw21676]
 probability distributions for an unlimited number of data streams and latent behavior states; 3) biased ... measurement error and temporallyirregular or missing data; 6) seamless integration of spatiotemporal covariate...

plsRglm
 Referenced in 3 articles
[sw12196]
 using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals...