nflreadr
The nflverse data loader, whose releases are dictated by the NFL calendar and CRAN's archive policy
Comparison · Analytics
e2tree vs trendseries
A side-by-side editorial comparison of e2tree and trendseries — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-package
e2tree vs trendseries: at a glance
What is e2tree?
The explainable-ensemble-tree package now measures whether its own explanations are faithful.
e2tree builds a single interpretable tree that approximates a fitted ensemble, working from the proximity structure the ensemble induces between observations. The 1.0.0 release added the piece that had been missing: a Goodness of Interpretability index quantifying how well the approximating tree reconstructs the ensemble's own proximity matrix, with a permutation test for significance. Interactive visualisation and a C++ backend with OpenMP parallelism arrived alongside, and support now spans ranger and CatBoost as well as the original targets.
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.
e2tree vs trendseries: editorial side-by-side
e2tree
0.0ANALYTICS
The explainable-ensemble-tree package now measures whether its own explanations are faithful.
◆ Current state
e2tree builds a single interpretable tree that approximates a fitted ensemble, working from the proximity structure the ensemble induces between observations. The 1.0.0 release added the piece that had been missing: a Goodness of Interpretability index quantifying how well the approximating tree reconstructs the ensemble's own proximity matrix, with a permutation test for significance. Interactive visualisation and a C++ backend with OpenMP parallelism arrived alongside, and support now spans ranger and CatBoost as well as the original targets.
◆ Where it's heading
Development has moved from producing an explanation to defending it. The GoI index and its permutation test change the package's claim from here is a tree that resembles your ensemble to here is how closely it resembles it and whether that could have happened by chance — the question a reviewer asks of any surrogate model. Around that, the work is engineering: the proximity matrix construction moved from R-level parallel loops into C++ with thread-level parallelism, and recent releases have been absorbing the awkwardness of supporting multiple ensemble backends, where a multi-class CatBoost objective returns a score matrix where a vector was expected. Interactive visNetwork output and standalone HTML export point at explanations meant to be shared rather than only inspected.
◆ Prediction
Given how much recent effort has gone into per-backend adapters, expect further work on ensemble compatibility; the entries do not indicate whether the interpretability index is heading toward comparing surrogate trees against each other.
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 e2tree 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 e2tree 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 e2tree alternatives → · See all trendseries alternatives →
Recent activity from e2tree and trendseries
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 15d agotrendseriesDecomposition becomes a first-class operation, five methods deep ⚡
- 3mo agoe2treeCatBoost multi-class and loss-function handling repaired
- 3mo agotrendseriesMulti-column trends and economically grounded UCM defaults
- 4mo agoe2treeA significance-tested measure of explanation fidelity ⚡
- 10mo agotrendseriesFirst production release with 21 trend extraction methods
- 1y agoe2treeranger models supported
Frequently asked questions
What is the difference between e2tree 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 e2tree 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 e2tree?
Top e2tree alternatives in Analytics are ranked by recent ship velocity. Browse the "e2tree alternatives" section above for the current picks, or visit /alternatives/e2tree 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.