tidytlg
A tables-listings-graphs package that reached CRAN and then went quiet.
A side-by-side editorial comparison of chattr and lightr — release velocity, themes, recent moves, and the top alternatives to consider.
chattr deleted every LLM integration it had written and outsourced the lot to ellmer
chattr puts a large language model inside the RStudio IDE, either through a Shiny app or directly at the console. As of 0.3.0 it no longer talks to any model provider itself: all integration goes through ellmer, and the hand-written OpenAI, Databricks and LlamaGPT backends were removed. The package's supported model list is now whatever ellmer supports, and the Shiny app streams responses through ellmer rather than managing a background process.
Reorganised its parsers by vendor, then opened the parser slot to users.
lightr reads spectrometry files from the proprietary formats that instrument vendors ship, and its recent releases have been about the structure of that parser collection rather than adding one more format. Version 2.0.0 renamed every low-level parser from lr_parse_<extension>() to lr_parse_<brand>_<extension>(), a breaking change made specifically so two vendors can share a file extension without colliding, and restored binary parsing for Avantes AvaSoft 8.4 using vendor-supplied format documentation. Version 2.1.0 follows through by exposing a parser argument on the high-level functions.
chattr puts a large language model inside the RStudio IDE, either through a Shiny app or directly at the console. As of 0.3.0 it no longer talks to any model provider itself: all integration goes through ellmer, and the hand-written OpenAI, Databricks and LlamaGPT backends were removed. The package's supported model list is now whatever ellmer supports, and the Shiny app streams responses through ellmer rather than managing a background process.
The first two releases show why that happened. Each provider brought its own error formats, token discovery and response handling, and 0.2.0 is largely a list of per-provider repairs — OpenAI error parsing, Copilot token discovery and model defaults, a new Databricks foundation model backend. Maintaining that surface scales linearly with the number of providers, and the pivot to ellmer trades it for a single dependency. The cost shows up immediately in 0.3.1, which exists solely to absorb a change in ellmer's token object.
Expect chattr's releases to now track ellmer's, as 0.3.1 already does, with the package's own work concentrating on the IDE experience rather than model connectivity. New provider support will arrive without a chattr release at all.
lightr reads spectrometry files from the proprietary formats that instrument vendors ship, and its recent releases have been about the structure of that parser collection rather than adding one more format. Version 2.0.0 renamed every low-level parser from lr_parse_<extension>() to lr_parse_<brand>_<extension>(), a breaking change made specifically so two vendors can share a file extension without colliding, and restored binary parsing for Avantes AvaSoft 8.4 using vendor-supplied format documentation. Version 2.1.0 follows through by exposing a parser argument on the high-level functions.
The package is moving from a fixed set of formats it knows about to a dispatch system users can extend. The brand-qualified naming and the parser argument are two halves of the same design: name parsers unambiguously, then let callers select or supply one. Alongside that runs steady attention to metadata fidelity — measurement timestamps, checksum verification against tampering, and timezone handling that survived upstream tzdata removing legacy codes.
Expect additional vendor parsers to arrive under the new brand-qualified scheme, and the custom-parser path to absorb formats the maintainers do not want to support directly. The entries do not name specific instruments planned next.
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 chattr or lightr.
A tables-listings-graphs package that reached CRAN and then went quiet.
Tplyr made clinical summary tables explain where every number came from.
Clinical listings that keep inheriting their hardest problem — pagination — from the layer below.
A cache-directory helper that has shipped nothing but CRAN-triggered patches for seven years.
gigs redesigned its whole conversion API for rOpenSci, then spent three releases getting the docs to build.
A weather-data client that keeps rewriting its HTTP layer while slowly tightening its API.
See all chattr alternatives → · See all lightr alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. chattr and lightr 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. chattr and lightr 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 chattr alternatives in Analytics are ranked by recent ship velocity. Browse the "chattr alternatives" section above for the current picks, or visit /alternatives/chattr for the full list with editorial commentary on each.
Top lightr alternatives in Analytics are ranked by recent ship velocity. Browse the "lightr alternatives" section above for the current picks, or visit /alternatives/lightr for the full list with editorial commentary on each.