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distributions3 vs semmcci

A side-by-side editorial comparison of distributions3 and semmcci — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:r-package

distributions3 vs semmcci: at a glance

Featuredistributions3semmcci
SectorAnalyticsAnalytics
Velocity score6.30.0
Sparks · 30d10
Top themesr-package, probability-distributions, empirical-distributions, likelihood-inferencestructural-equation-modeling, monte-carlo, confidence-intervals, r-package
Last editorial update1h ago2d ago
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What is distributions3?

distributions3 0.3.0 adds sample-based distributions and likelihood derivatives

An R package giving probability distributions a consistent object interface - d/p/q/r functions, moments, and prodist() methods that pull a fitted distribution out of a regression object. Version 0.3.0 is the first substantive release under Achim Zeileis's maintenance, and it widens what a distribution is allowed to be: Empirical() represents a distribution by a random sample rather than by parameters, and numerical fallbacks now fill in cdf(), pdf(), quantile(), random() and the moments for any object that implements only some of them. New score() and hessian() generics compute first and second derivatives of the log-likelihood with respect to the parameters, analytically for a few distributions and numerically for the rest.

Read the full distributions3 trajectory →

What is semmcci?

Monte Carlo confidence intervals for SEM, now mostly reacting to upstream deprecations

semmcci generates Monte Carlo confidence intervals for structural equation model parameters, working alongside lavaan. Its four-year release history is a run of patch versions from the jeksterslab account, each adding a function or adjusting method detail. The recent ones are quieter still: the latest addresses a lavaan::getCov() deprecation in tests, and the one before it is described only as minor method edits.

Read the full semmcci trajectory →

distributions3 vs semmcci: editorial side-by-side

D6.3

distributions3 0.3.0 adds sample-based distributions and likelihood derivatives

◆ Current state

An R package giving probability distributions a consistent object interface - d/p/q/r functions, moments, and prodist() methods that pull a fitted distribution out of a regression object. Version 0.3.0 is the first substantive release under Achim Zeileis's maintenance, and it widens what a distribution is allowed to be: Empirical() represents a distribution by a random sample rather than by parameters, and numerical fallbacks now fill in cdf(), pdf(), quantile(), random() and the moments for any object that implements only some of them. New score() and hessian() generics compute first and second derivatives of the log-likelihood with respect to the parameters, analytically for a few distributions and numerically for the rest.

◆ Where it's heading

Growth used to arrive as new distribution families contributed from outside - the extreme-value set, Erlang, later the Poisson binomial. This release changes the axis: alongside two new distributions it adds an inference layer (score, hessian) and a forecast-evaluation one (crps() methods against scoringRules), which are capabilities about distributions rather than more of them. Dependency weight is being cut at the same time, with ggplot2 demoted to Suggests and glue replaced by base R sprintf().

◆ Prediction

With numeric fallbacks and the derivative generics in place, expect analytic score() and hessian() methods to be filled in across more of the distribution catalogue. The constructor-default change is the likeliest source of follow-up fixes, since calls like Poisson() now return a length-zero distribution where they previously errored.

S
semmcci
ANALYTICS
0.0

Monte Carlo confidence intervals for SEM, now mostly reacting to upstream deprecations

◆ Current state

semmcci generates Monte Carlo confidence intervals for structural equation model parameters, working alongside lavaan. Its four-year release history is a run of patch versions from the jeksterslab account, each adding a function or adjusting method detail. The recent ones are quieter still: the latest addresses a lavaan::getCov() deprecation in tests, and the one before it is described only as minor method edits.

◆ Where it's heading

The functional build-out finished some time ago. MCGeneric() in 1.1.3 and Func()/MCFunc() in 1.1.4 opened the package to user-defined functions of parameters, which is the natural end point for a Monte Carlo interval tool — once arbitrary functions are supported, there is little left to add. Since then releases have tracked lavaan's changes rather than semmcci's own direction, and the gap between them has stretched from months to over a year.

◆ Prediction

Expect the next release to be triggered by another lavaan deprecation rather than by new capability.

Alternatives to distributions3 and semmcci

Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either distributions3 or semmcci.

See all distributions3 alternatives → · See all semmcci alternatives →

Recent activity from distributions3 and semmcci

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1h agodistributions3Empirical distributions, plus score and hessian generics
  2. 28d agodistributions3Maintenance moves to Achim Zeileis; moment calculations corrected
  3. 2mo agosemmccilavaan getCov() deprecation handled in tests
  4. 10mo agosemmcciMinor method edits
  5. 10mo agodistributions3ggplot2 compatibility for the plotting functions
  6. 1y agodistributions3Poisson binomial distribution, with a normal-approximation fallback
  7. 2y agosemmcciUser-defined parameter functions via Func() and MCFunc()
  8. 2y agosemmcciMCGeneric() opens up arbitrary parameter targets
  9. 3y agosemmcciMultiple-imputation support via MCMI()
  10. 3y agosemmcciData generation internals refactored
  11. 3y agodistributions3is_discrete and is_continuous generics, plus elementwise type-safety
  12. 4y agodistributions3Extreme-value family, Erlang, and a plotting generic

Frequently asked questions

What is the difference between distributions3 and semmcci?

Both compete on the same themes — r-package — within Analytics. distributions3 is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is distributions3 better than semmcci?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. distributions3 is currently shipping more aggressively (velocity 6.3 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to distributions3?

Top distributions3 alternatives in Analytics are ranked by recent ship velocity. Browse the "distributions3 alternatives" section above for the current picks, or visit /alternatives/distributions3-r for the full list with editorial commentary on each.

What are the best alternatives to semmcci?

Top semmcci alternatives in Analytics are ranked by recent ship velocity. Browse the "semmcci alternatives" section above for the current picks, or visit /alternatives/semmcci for the full list with editorial commentary on each.