• Mplus

  • Referenced in 244 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 60 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 19 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 time-series-cross-sectional 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...
  • Netica

  • Referenced in 9 articles [sw13961]
  • ordinary tab-delimited form and have ”missing data...
  • Mx

  • Referenced in 9 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...
  • 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...
  • 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...
  • StatMatch

  • Referenced in 16 articles [sw11933]
  • also be used to impute missing values in data sets through hot deck imputation methods...
  • 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 23 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...
  • 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...
  • repolr

  • Referenced in 4 articles [sw07133]
  • first-order 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 temporally-irregular or missing data; 6) seamless integration of spatio-temporal covariate...
  • BradleyTerry2

  • Referenced in 6 articles [sw09554]
  • probit or cauchit models to pair-comparison data. Included are the standard ‘unstructured’ Bradley-Terry ... simple and efficient approach to handling missing covariate data, and suitably-defined residuals for diagnostic...
  • plsRglm

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