PINstimation
A market-microstructure toolkit that keeps adding estimators as the papers land.
Comparison · Infra & APIs
mdatools vs mice
A side-by-side editorial comparison of mdatools and mice — release velocity, themes, recent moves, and the top alternatives to consider.
mdatools vs mice: at a glance
What is mdatools?
mdatools spun out its cross-validation method, then came back for three-way data.
mdatools is a long-running chemometrics package covering PCA, PLS regression, SIMCA and DD-SIMCA classification, MCR resolution and a large spectral preprocessing framework. Its releases are infrequent and each one tends to carry one substantive idea plus a handful of fixes. The June release opens a direction the package had not previously taken: DD-SIMCA classification of three-way data, through PARAFAC and Tucker decompositions.
What is mice?
mice can finally predict, not just estimate, from multiply imputed data.
mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.
mdatools vs mice: editorial side-by-side
mdatools
0.0INFRA · APIS
mdatools spun out its cross-validation method, then came back for three-way data.
◆ Current state
mdatools is a long-running chemometrics package covering PCA, PLS regression, SIMCA and DD-SIMCA classification, MCR resolution and a large spectral preprocessing framework. Its releases are infrequent and each one tends to carry one substantive idea plus a handful of fixes. The June release opens a direction the package had not previously taken: DD-SIMCA classification of three-way data, through PARAFAC and Tucker decompositions.
◆ Where it's heading
The shape of the package has been managed deliberately rather than allowed to sprawl. Procrustes cross-validation grew large enough to warrant its own package and was moved out to pcv in 0.14.0; preprocessing was consolidated in 0.12.0 into a composable prep() framework rather than a set of loose functions. Around that, the recurring work is numerical: a more stable SIMPLS implementation, cross-validation rewritten to accept user-supplied segment indices, prep.savgol() and prep.alsbasecorr() rewritten for speed, and now the baseline iteration default raised to match the web applications the maintainer also runs.
◆ Prediction
Three-way DD-SIMCA arrives with two decompositions and no companion regression or resolution methods for multiway data, so extending the multiway path to the rest of the toolkit is the obvious follow-up. The alignment of defaults with the maintainer's web applications suggests those two codebases will keep being reconciled.
mice
0.0INFRA · APIS
mice can finally predict, not just estimate, from multiply imputed data.
◆ Current state
mice is the reference implementation of multiple imputation by chained equations, and the default answer to missing data in R. The releases here follow a consistent shape: one or two substantive additions per version, most contributed by outside authors, plus fixes to methods that have been in the package for years. The current 3.19.0 adds predict_mi(), which pools predictions across imputations under Rubin's rules and can return prediction intervals.
◆ Where it's heading
Two things are happening. The imputation method catalogue keeps widening — lasso variants, multivariate PMM, categorical PMM via canonical correlation — while the pooling side is being extended past its original purpose, first to synthetic data, now to predictions on held-out sets. That second thread points at predictive modelling workflows rather than the inferential ones mice was built for. Meanwhile the maintainers keep finding consequential old bugs: the augment() ordered-factor defect in 3.18.0 had been silently degrading ordinal imputations for years.
◆ Prediction
predict_mi() is framed around evaluating predictive performance on test sets, and the ignore argument added in 3.12.0 already exists to hold out rows from the imputation model. Expect the next work to join those up into a fuller train/test story for imputed data, since the pieces are now in place but not yet connected.
Alternatives to mdatools and mice
Other Infra & APIs 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 mdatools or mice.
nzilbb.vowels
A vowel-analysis package trimming dependencies after an email address got it archived.
emuR
The R half of the EMU speech database system, fixing what was quietly broken.
BAS
A Bayesian model-averaging package spending its 2.0 on memory, not methods.
tidyplots
tidyplots keeps rebuilding its own foundations rather than layering around them.
GencoDymo2
A GENCODE annotation toolkit spent its first year getting out of CRAN's way.
See all mdatools alternatives → · See all mice alternatives →
Recent activity from mdatools and mice
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agomdatoolsDD-SIMCA classification arrives for three-way data ⚡
- 5mo agomdatoolsv. 0.15.0
- 8mo agomicemice 3.19.0 ⚡
- 1y agomicemice 3.18.0
- 1y agomicemice 3.17.0
- 2y agomdatoolsData frames converted to matrices automatically for model training
- 3y agomdatoolscv.scope lets centering and scaling follow the global or local set
- 3y agomicemice 3.16.0
- 3y agomdatoolsProcrustes cross-validation moves out to its own pcv package ⚡
- 3y agomicemice 3.15.0
- 3y agomdatoolsgetRegcoeffs() fixed for unscaled models; ipls() gains a full mode
- 4y agomicemice 3.14.0
Frequently asked questions
What is the difference between mdatools and mice?
They serve adjacent needs but don't currently overlap on shipped themes. mdatools and mice are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is mdatools better than mice?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mdatools and mice are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Infra & APIs products to evaluate alongside.
What are the best alternatives to mdatools?
Top mdatools alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "mdatools alternatives" section above for the current picks, or visit /alternatives/mdatools for the full list with editorial commentary on each.
What are the best alternatives to mice?
Top mice alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "mice alternatives" section above for the current picks, or visit /alternatives/mice for the full list with editorial commentary on each.