simlandr
Potential landscape tooling settling onto standard R generics after two rounds of renaming.
A side-by-side editorial comparison of delaporte and quanteda.textmodels — release velocity, themes, recent moves, and the top alternatives to consider.
A Fortran-backed Delaporte distribution package where every release is compiler and CRAN weather.
Delaporte supplies the density, distribution, quantile and random-generation functions for the Delaporte distribution — a Poisson-negative-binomial convolution used for overdispersed count data — implemented in Fortran and called through C. The only user-facing addition in the visible history is explicit OpenMP thread control via getDelapThreads() and setDelapThreads() at 8.2.0.
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.
Delaporte supplies the density, distribution, quantile and random-generation functions for the Delaporte distribution — a Poisson-negative-binomial convolution used for overdispersed count data — implemented in Fortran and called through C. The only user-facing addition in the visible history is explicit OpenMP thread control via getDelapThreads() and setDelapThreads() at 8.2.0.
The maintenance burden here is portability, not statistics. Recent entries track a Fortran suffix change for Intel compiler compatibility, architecture-specific test tolerances, type-safety corrections on values crossing the C-to-Fortran boundary, and a thread-count variable relocated from R options to an environment variable to follow an upstream R commit. The distribution functions themselves are settled; what changes is how the compiled code is built and checked across CRAN's platform matrix.
Expect the next release to follow another CRAN toolchain or Writing R Extensions policy change, as the last several have. Two of the four visible entries carry no notes at all, so this feed will keep understating what actually shipped.
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 delaporte 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 delaporte 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. delaporte 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. delaporte 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 delaporte alternatives in Analytics are ranked by recent ship velocity. Browse the "delaporte alternatives" section above for the current picks, or visit /alternatives/delaporte 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.