mice
mice can finally predict, not just estimate, from multiply imputed data.
A side-by-side editorial comparison of standardlastprofile and stochvol — release velocity, themes, recent moves, and the top alternatives to consider.
A German electricity load-profile package added gas and doubled the market it serves.
standardlastprofile implements the BDEW standard load profiles that German utilities use to assign consumption to customers without interval metering. Until June it did electricity only. Version 2.0.0 added the gas side — the SigLinDe synthetic procedure across all 15 BDEW gas profile IDs — and gave electricity a new primary interface, slp_electricity(), with slp_generate() superseded but retained.
A Bayesian volatility sampler in its maintenance decade, paying for its own speed
stochvol runs MCMC for stochastic volatility models, with a C++ sampler underneath an R interface. Five years of releases in this window contain no new models: the work is compiler and dependency compatibility, CRAN check notes, and a steady trickle of corrections to the sampler itself. Its methodological milestone, the Journal of Statistical Software paper, is recorded in a 2021 tag.
standardlastprofile implements the BDEW standard load profiles that German utilities use to assign consumption to customers without interval metering. Until June it did electricity only. Version 2.0.0 added the gas side — the SigLinDe synthetic procedure across all 15 BDEW gas profile IDs — and gave electricity a new primary interface, slp_electricity(), with slp_generate() superseded but retained.
The package is converting from a dataset wrapper into a calculation library. Electricity profiles are tabulated values the package ships; gas profiles are computed from daily temperatures and a customer value through a coefficient-driven function, and the maintainer exposed the whole ladder — slp_gas() for the profile, slp_gas_kundenwert() to derive the customer value from a reference year, slp_gas_siglinde() for the raw h(theta) demand function so users can supply state-level coefficients, and coefficient and weekday-factor accessors underneath. The same instinct removed the built-in holiday table in favour of computing Easter directly, which lifted the date range cap from 2073 to open-ended. Deprecations are handled carefully throughout: renames keep working with lifecycle warnings, and the one hard break was already a warning since 1.1.0.
slp_gas_siglinde() was exported specifically so users could plug in region-specific coefficients such as Baden-Wurttemberg's, which points at state-level coefficient sets as the next thing to ship rather than leave to callers. The BDEW reference edition is now pinned to an Internet Archive permalink after the last one 404'd, so tracking edition changes is an ongoing maintenance cost.
stochvol runs MCMC for stochastic volatility models, with a C++ sampler underneath an R interface. Five years of releases in this window contain no new models: the work is compiler and dependency compatibility, CRAN check notes, and a steady trickle of corrections to the sampler itself. Its methodological milestone, the Journal of Statistical Software paper, is recorded in a 2021 tag.
This is what a finished computational package looks like. The formula interface arrived at 3.1.0 and nothing has been added since; what changes is the ground underneath — RcppArmadillo major versions, UBSan checks, error-handling conventions moving from Rf_error to Rcpp::stop for correct memory management. The recurring pattern worth watching is that several releases fix real errors in the sampler's proposal distributions, found by users and by CRAN's own instrumented checks rather than by the maintainer.
Nothing in these notes suggests new methodology. Expect the next release when RcppArmadillo or a CRAN check flavour forces one, and treat any bug report against the samplers as the more consequential event.
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 standardlastprofile or stochvol.
mice can finally predict, not just estimate, from multiply imputed data.
A market-microstructure toolkit that keeps adding estimators as the papers land.
A vowel-analysis package trimming dependencies after an email address got it archived.
The R half of the EMU speech database system, fixing what was quietly broken.
A Bayesian model-averaging package spending its 2.0 on memory, not methods.
tidyplots keeps rebuilding its own foundations rather than layering around them.
See all standardlastprofile alternatives → · See all stochvol alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. standardlastprofile and stochvol 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. standardlastprofile and stochvol 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.
Top standardlastprofile alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "standardlastprofile alternatives" section above for the current picks, or visit /alternatives/standardlastprofile for the full list with editorial commentary on each.
Top stochvol alternatives in Infra & APIs are ranked by recent ship velocity. Browse the "stochvol alternatives" section above for the current picks, or visit /alternatives/stochvol for the full list with editorial commentary on each.