StataCorp Stata MP 16.0

Stata/MP lets you analyze data in one-half to two-thirds the time compared with Stata/SE on inexpensive dual-core laptops and in one-quarter to one-half the time on quad-core desktops and laptops.
StataCorp Stata MP 16.0
5.0/5 Votes: 2
Author
StataCorp LLC
Version
16.0
Updated
October 4, 2021
Requirements
Windows x64 or x86
Size
362.76 MB
Download
6218

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Description

Stata/MP lets you analyze data in one-half to two-thirds the time compared with Stata/SE on inexpensive dual-core laptops and in one-quarter to one-half the time on quad-core desktops and laptops. Stata/MP runs even faster on multiprocessor servers. Stata/MP supports up to 64 cores/processors.

Stata/SE can analyze up to 2 billion observations. Stata/MP can analyze 10 to 20 billion observations on the largest computers currently available and is ready to analyze up to 1 trillion observations once computer hardware catches up. Stata/MP also allows 120,000 variables compared to 32,767 variables allowed by Stata/SE.

Some procedures are not parallelized and some are inherently sequential, meaning they run the same speed in Stata/MP. For a complete assessment of Stata/MP’s performance, including command-by-command statistics.

Stata/MP is the multiprocessor and multicore version of Stata. It’s primary purpose is to run faster. Most of the new features in Stata have been parallelized to run faster on Stata/MP, sometimes much faster.

Features

  • Bayesian multilevel models
  • Panel-data tobit with random co-efficients
  • Multilevel regression for interval-censored data
  • Multilevel tobit regression for censored data
  • Panel-data cointegration tests
  • Tests for multiple breaks in time series
  • Multiple-group SEM for continuous, binary, ordered, and count outcomes
  • Multiple-group multilevel SEM
  • ICD-10-CM/PCS
  • Power analysis for cluster randomized designs
  • Power analysis for linear regression models
  • Heteroskedastic linear regression
  • Poisson with sample selection
  • Zero-inflated ordered probit
  • Add your own power and sample-size methods
  • Transparency on graphs
  • Stream random-number generator

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