
Mplus
 Referenced in 227 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 54 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 14 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 9 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...

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

LogXact
 Referenced in 6 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...

Netica
 Referenced in 8 articles
[sw13961]
 ordinary tabdelimited form and have ”missing data...

Monomvn
 Referenced in 4 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...

StatMatch
 Referenced in 10 articles
[sw11933]
 also be used to impute missing values in data sets through hot deck imputation methods...

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...

PDB2PQR
 Referenced in 19 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...

repolr
 Referenced in 3 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...

rsem
 Referenced in 3 articles
[sw06771]
 Robust structural equation modeling with missing data and auxiliary variables The paper develops...

simFrame
 Referenced in 3 articles
[sw08021]
 guidelines regarding, e.g., simulation designs, contamination, missing data models or evaluation criteria are necessary...

MULTIMIX
 Referenced in 23 articles
[sw03250]
 multivariate data that have categorical and continuous variables and that possibly contain missing values. This...

TASSEL
 Referenced in 2 articles
[sw11446]
 phenotypic and genotypic data, imputing missing data and calculating principal components. Availability: The TASSEL executable...

NVIVO
 Referenced in 8 articles
[sw14072]
 need NVivo  powerful software for qualitative data analysis. Whether you are working individually ... afford to miss the insights your data is trying to show...

BaBooN
 Referenced in 1 article
[sw19481]
 Predictive Mean Matching to multiply impute missing data. The first variant is a variable ... that has been extended for unordered categorical data. The Bayesian Bootstrap allows for generating approximately ... this variant, if the missingdata pattern resembles a data fusion situation, or any other ... pattern, where several variables have identical missingdata patterns. Both variants...

S+ FDA
 Referenced in 3 articles
[sw10814]
 handle irregularly spaced data or data with missing values. For large amounts of data, working...

BAMD
 Referenced in 1 article
[sw17319]
 identification in association studies with missing data. Association testing aims to discover the underlying relationship ... typically large data sets used in association testing often contain missing values. Standard statistical methods ... hierarchical model BAMD (Bayesian Association with Missing Data). BAMD is a Gibbs sampler, in which ... effects are recovered with missing genotype data. Also, we validate associations between SNPs...