• Mplus

  • Referenced in 273 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...
  • Amelia

  • Referenced in 16 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...
  • XGobi

  • Referenced in 22 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 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...
  • Mx

  • Referenced in 12 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 10 articles [sw13961]
  • ordinary tab-delimited form and have ”missing data...
  • 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 27 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 10 articles [sw20451]
  • model (HMM) technology for dealing with missing genotype data. We have implemented the main...
  • VIM

  • Referenced in 6 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...
  • BradleyTerry2

  • Referenced in 9 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...
  • 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...
  • TASSEL

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

  • Referenced in 5 articles [sw08021]
  • guidelines regarding, e.g., simulation designs, contamination, missing data models or evaluation criteria are necessary...
  • 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...