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Comparison · Analytics
reliaplotr vs trendseries
A side-by-side editorial comparison of reliaplotr and trendseries — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
reliaplotr vs trendseries: at a glance
What is reliaplotr?
The Weibull plotting package renamed itself, then handed its charts to AI assistants.
ReliaPlotR draws interactive reliability plots with plotly — probability plots, contour plots, Duane and reliability growth charts, accelerated life testing plots by stress level, mean cumulative function curves for repairable systems, and exposure plots. It was WeibullR.plotly until late 2025, and the rename tracked a real widening of scope rather than just a label change. The current release adds tidy extractors that turn fitted model objects into data frames, and an MCP server exposing five of its fit and plot functions as tools.
What is trendseries?
A trend-extraction toolkit grows a full decomposition engine, seasonal components and all.
trendseries extracts trends from economic time series through two pipe-friendly functions, backed by an unusually broad method set — Hodrick-Prescott in one- and two-sided variants, Baxter-King, Christiano-Fitzgerald, Hamilton regression, Beveridge-Nelson, unobserved components, plus the moving average and smoothing family. The 1.4 release adds decomposition proper: an exported decompose_series() that splits a series into trend, seasonal, and remainder across five methods and guarantees the components add back to the original values.
reliaplotr vs trendseries: editorial side-by-side
reliaplotr
0.0ANALYTICS
The Weibull plotting package renamed itself, then handed its charts to AI assistants.
◆ Current state
ReliaPlotR draws interactive reliability plots with plotly — probability plots, contour plots, Duane and reliability growth charts, accelerated life testing plots by stress level, mean cumulative function curves for repairable systems, and exposure plots. It was WeibullR.plotly until late 2025, and the rename tracked a real widening of scope rather than just a label change. The current release adds tidy extractors that turn fitted model objects into data frames, and an MCP server exposing five of its fit and plot functions as tools.
◆ Where it's heading
The package has been following its analysis siblings function for function: as accelerated life testing and repairable systems modelling landed in the wider suite, the matching plot types appeared here, and when the growth-analysis package shipped an MCP server, this one followed two weeks later. The tidy extractors point the same way — a plotting package that can also return parameter estimates, goodness-of-fit metrics, and confidence bounds as tidy frames is one designed to be consumed programmatically, by a pipeline or an assistant, not only read on screen. Overlaying multiple model fits on a single plot has been a recurring request answered across several releases.
◆ Prediction
Expect the tidy extractor and MCP tool surfaces to keep expanding together, since each new plot type in the suite now implies both a chart and a machine-readable version of what it shows.
trendseries
3.8ANALYTICS
A trend-extraction toolkit grows a full decomposition engine, seasonal components and all.
◆ Current state
trendseries extracts trends from economic time series through two pipe-friendly functions, backed by an unusually broad method set — Hodrick-Prescott in one- and two-sided variants, Baxter-King, Christiano-Fitzgerald, Hamilton regression, Beveridge-Nelson, unobserved components, plus the moving average and smoothing family. The 1.4 release adds decomposition proper: an exported decompose_series() that splits a series into trend, seasonal, and remainder across five methods and guarantees the components add back to the original values.
◆ Where it's heading
The package is moving from breadth of methods to rigour about what those methods produce. Recent work has been about defaults and guarantees rather than new filters: the unobserved components model now derives its signal-to-noise ratios from Hodrick-Prescott lambdas so the default output is economically interpretable, decomposition carries an exact additive identity, and a log transform gives a uniform multiplicative variant across every method. Naming is being tidied in the same spirit, with group_vars deprecated in favour of group_cols. Side-by-side method comparison — passing several methods and getting each one's components as separate columns — suggests an audience that treats method choice as a research question rather than a setting.
◆ Prediction
Expect the comparison and diagnostic side to keep developing, since the package now produces multiple decompositions of the same series and offers no ranking between them; the entries give no indication of new filters being queued.
Alternatives to reliaplotr and trendseries
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 reliaplotr or trendseries.
susier
Fine-mapping workhorse susieR spends its releases hunting null-effect trimming bugs
UCell
A rank-based gene signature scorer that has grown by adapting to whatever object format single-cell R uses next
detectseparation
A diagnostic package that generalized past its own name, then learned to say which kind of separation it found
brglm2
A bias-reduction package reaches 1.0 by adding an estimator built for high-dimensional logistic regression
bayestools
The JAGS toolkit under RoBMA, shipping the standardization machinery its downstream rewrite needed
See all reliaplotr alternatives → · See all trendseries alternatives →
Recent activity from reliaplotr and trendseries
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 15d agotrendseriesDecomposition becomes a first-class operation, five methods deep ⚡
- 2mo agoreliaplotrTidy model extractors plus five plotting tools over MCP ⚡
- 2mo agoreliaplotrNHPP plots switch to the mean cumulative function
- 3mo agotrendseriesMulti-column trends and economically grounded UCM defaults
- 4mo agoreliaplotrAccelerated life testing and repairable systems plots added
- 8mo agoreliaplotrDuane plots gain confidence bounds
- 9mo agoreliaplotrReliaPlotR v0.4.1
- 10mo agoreliaplotrRenamed from WeibullR.plotly to ReliaPlotR
- 10mo agotrendseriesFirst production release with 21 trend extraction methods
Frequently asked questions
What is the difference between reliaplotr and trendseries?
Both compete on the same themes — r-package — within Analytics. trendseries is currently shipping more aggressively (velocity 3.8 vs 0.0), with 1 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is reliaplotr better than trendseries?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. trendseries is currently shipping more aggressively (velocity 3.8 vs 0.0), with 1 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
What are the best alternatives to reliaplotr?
Top reliaplotr alternatives in Analytics are ranked by recent ship velocity. Browse the "reliaplotr alternatives" section above for the current picks, or visit /alternatives/reliaplotr for the full list with editorial commentary on each.
What are the best alternatives to trendseries?
Top trendseries alternatives in Analytics are ranked by recent ship velocity. Browse the "trendseries alternatives" section above for the current picks, or visit /alternatives/trendseries for the full list with editorial commentary on each.