simlandr
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
A side-by-side editorial comparison of incase and quanteda.textmodels — release velocity, themes, recent moves, and the top alternatives to consider.
A safer case_when that keeps hardening its guarantees while realigning to tidyverse naming.
incase supplies in_case(), switch_case(), grep_case() and fn_case() as vectorised recoding functions in the dplyr::case_when idiom, with _fct and _list variants that return factors or lists instead of forcing atomic type conversion. The 0.4.0 release deprecates the undotted preserve, default and ordered arguments in favour of dotted forms, starting a removal clock, and adds .exhaustive to error on unmatched inputs.
Split out of quanteda, then quiet - one new classifier since 2020.
quanteda.textmodels holds the scaling and classification models factored out of quanteda's main package. The visible history is thin: a logistic regression classifier and a native C++ rewrite of svmlin in late 2020, an SVM default change in early 2021, and after that only compatibility work. The most recent release fixes a namespace break caused by quanteda 4.1.0 dropping RcppArmadillo.
incase supplies in_case(), switch_case(), grep_case() and fn_case() as vectorised recoding functions in the dplyr::case_when idiom, with _fct and _list variants that return factors or lists instead of forcing atomic type conversion. The 0.4.0 release deprecates the undotted preserve, default and ordered arguments in favour of dotted forms, starting a removal clock, and adds .exhaustive to error on unmatched inputs.
The arc is consistently toward catching recoding mistakes at the call site rather than letting them pass silently. Early releases broadened how a match can be expressed — pattern matching, function application, factor and list returns. Recent work has shifted to guarantees about the result: correct factor level ordering relative to .default, and now an exhaustiveness check. Notably 0.4.0 reverses the 0.3.2 decision to accept arguments with or without dots, trading that flexibility for namespace safety against user-supplied case names.
The deprecation warnings introduced in 0.4.0 point to a follow-up release that removes the undotted arguments outright. Whether .exhaustive eventually becomes the default is unclear from these entries.
quanteda.textmodels holds the scaling and classification models factored out of quanteda's main package. The visible history is thin: a logistic regression classifier and a native C++ rewrite of svmlin in late 2020, an SVM default change in early 2021, and after that only compatibility work. The most recent release fixes a namespace break caused by quanteda 4.1.0 dropping RcppArmadillo.
The package now moves when its parent or a dependency moves, not on its own schedule. Four of the six most recent releases exist to track changes in quanteda, Matrix, or CRAN policy. The modelling decisions that were made - defaulting textmodel_svm() to the L2-regularized L2-loss dual solver, reducing svmlin to a single algorithm - have not been revisited since.
The next release most likely follows another upstream change in quanteda or a Matrix and Rcpp dependency rather than adding a model.
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 incase or quanteda.textmodels.
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
SEM reporting helpers converging on APA output, one CRAN resubmission at a time.
A raster-to-terra migration is the only readable change in a feed of merge notes.
A nycflights13 generator whose recent work is all about the data being right.
Conditional density and log-likelihood fill out a vine copula regression package.
A drop-in string API for base R, kept alive by upstream check failures.
See all incase alternatives → · See all quanteda.textmodels alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
Both compete on the same themes — r-package — within Analytics. incase and quanteda.textmodels 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. incase and quanteda.textmodels 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 Analytics products to evaluate alongside.
Top incase alternatives in Analytics are ranked by recent ship velocity. Browse the "incase alternatives" section above for the current picks, or visit /alternatives/incase for the full list with editorial commentary on each.
Top quanteda.textmodels alternatives in Analytics are ranked by recent ship velocity. Browse the "quanteda.textmodels alternatives" section above for the current picks, or visit /alternatives/quanteda-textmodels for the full list with editorial commentary on each.