Stata is a complete, integrated statistical package that provides everything you need for data analysis, data management, and graphics. Stata is not sold in modules, which means you get everything you need in one package. And, you can choose a perpetual license, with nothing more to buy ever. Annual licenses are also available.Stata 12 adds many new features such as structural equation modeling (SEM), contrasts, ARFIMA, business calendars, chained equations for multiple imputation, contour plots, automatic memory management, importing and exporting of Excel files, and more. (Source:

References in zbMATH (referenced in 272 articles , 1 standard article )

Showing results 1 to 20 of 272.
Sorted by year (citations)

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  1. Drton, Mathias; Fox, Christopher; Wang, Y. Samuel: Computation of maximum likelihood estimates in cyclic structural equation models (2019)
  2. Shieh, Gwowen: Effect size, statistical power, and sample size for assessing interactions between categorical and continuous variables (2019)
  3. Stockemer, Daniel: Quantitative methods for the social sciences. A practical introduction with examples in SPSS and Stata (2019)
  4. Thomas Jaki; Philip Pallmann; Dominic Magirr: The R Package MAMS for Designing Multi-Arm Multi-Stage Clinical Trials (2019) not zbMATH
  5. Yiyun Shou and Michael Smithson: cdfquantreg: An R Package for CDF-Quantile Regression (2019) not zbMATH
  6. Agresti, Alan: An introduction to categorical data analysis (2018)
  7. Alberto Garcia-Hernandez; Dimitris Rizopoulos: %JM: A SAS Macro to Fit Jointly Generalized Mixed Models for Longitudinal Data and Time-to-Event Responses (2018) not zbMATH
  8. Daniel Heck and Morten Moshagen: RRreg: An R Package for Correlation and Regression Analyses of Randomized Response Data (2018) not zbMATH
  9. Delia Voronca; Mulugeta Gebregziabher; Valerie Durkalski-Mauldin; Lei Liu; Leonard Egede: MTPmle: A SAS Macro and Stata Programs for Marginalized Inference in Semi-Continuous Data (2018) not zbMATH
  10. Diane Uschner; David Schindler; Ralf-Dieter Hilgers; Nicole Heussen: randomizeR: An R Package for the Assessment and Implementation of Randomization in Clinical Trials (2018) not zbMATH
  11. Felix Pretis; J. Reade; Genaro Sucarrat: Automated General-to-Specific (GETS) Regression Modeling and Indicator Saturation for Outliers and Structural Breaks (2018) not zbMATH
  12. Imdad Ullah Muhammad, Aslam Muhammad: lmridge: A Comprehensive R Package for Ridge Regression (2018) not zbMATH
  13. Jingyi Guo; Andrea Riebler: meta4diag: Bayesian Bivariate Meta-Analysis of Diagnostic Test Studies for Routine Practice (2018) not zbMATH
  14. Marco Villegas; Diego Pedregal: SSpace: A Toolbox for State Space Modeling (2018) not zbMATH
  15. Mark Donoghoe; Ian Marschner: logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model (2018) not zbMATH
  16. Parisi, Antonio; Liseo, B.: Objective Bayesian analysis for the multivariate skew-(t) model (2018)
  17. Wyszynski, Karol; Marra, Giampiero: Sample selection models for count data in R (2018)
  18. Agnieszka Król; Audrey Mauguen; Yassin Mazroui; Alexandre Laurent; Stefan Michiels; Virginie Rondeau: Tutorial in Joint Modeling and Prediction: A Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event (2017) not zbMATH
  19. Canary, Jana D.; Blizzard, Leigh; Barry, Ronald P.; Hosmer, David W.; Quinn, Stephen J.: A comparison of the Hosmer-Lemeshow, Pigeon-Heyse, and Tsiatis goodness-of-fit tests for binary logistic regression under two grouping methods (2017)
  20. Clifford Anderson-Bergman: icenReg: Regression Models for Interval Censored Data in R (2017) not zbMATH

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