Sun, 15 Nov 2026

Package cgam updated to version 1.34 with previous version 1.33 dated 2026-10-20

Title: Constrained Generalized Additive Model
Description: A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user needs only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the cgam routine implements a two-dimensional isotonic regression using warped-plane splines without additivity assumptions. It can also fit a convex or concave regression surface with [...truncated...]
Author: Mary Meyer [aut], Xiyue Liao [aut, cre]
Maintainer: Xiyue Liao <xliao@sdsu.edu>

Diff between cgam versions 1.33 dated 2026-10-20 and 1.34 dated 2026-11-15

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More information about cgam at CRAN
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Package forestsearch updated to version 0.5.0 with previous version 0.4.0 dated 2026-10-10

Title: Exploratory Subgroup Identification in Survival and GLM Outcomes
Description: Implements statistical methods for exploratory subgroup identification in clinical trials. Provides tools for identifying patient subgroups with differential treatment effects using machine learning approaches including Generalized Random Forests (GRF), LASSO regularization, and exhaustive combinatorial search algorithms. Supports survival endpoints (Cox proportional hazards), binary outcomes (log odds ratio, log relative risk, risk difference), continuous outcomes (mean difference), and count / rate outcomes (log incidence rate ratio via Poisson, quasi-Poisson, or negative-binomial GLMs with optional person-time offset). Features bootstrap bias correction using infinitesimal jackknife methods to address selection bias in post-hoc analyses. Designed for clinical researchers conducting exploratory subgroup analyses in randomized controlled trials, particularly for multi-regional clinical trials (MRCT) requiring regional consistency evaluation. Methods are described in Leon et al. (2024) [...truncated...]
Author: Larry Leon [aut, cre]
Maintainer: Larry Leon <larry.leon.05@post.harvard.edu>

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More information about forestsearch at CRAN
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Package caret updated to version 7.1-0 with previous version 7.0-1 dated 2024-12-09

Title: Classification and Regression Training
Description: Misc functions for training and plotting classification and regression models.
Author: Max Kuhn [aut, cre] , Jed Wing [ctb], Steve Weston [ctb], Andre Williams [ctb], Chris Keefer [ctb], Allan Engelhardt [ctb], Tony Cooper [ctb], Zachary Mayer [ctb], Brenton Kenkel [ctb], R Core Team [ctb], Michael Benesty [ctb], Reynald Lescarbeau [ct [...truncated...]
Maintainer: Max Kuhn <mxkuhn@gmail.com>

Diff between caret versions 7.0-1 dated 2024-12-09 and 7.1-0 dated 2026-11-15

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 355 files changed, 26581 insertions(+), 19896 deletions(-)

More information about caret at CRAN
Permanent link

Package saqgetr (with last version 0.2.24) was removed from CRAN

Previous versions (as known to CRANberries) which should be available via the Archive link are:

2026-07-03 0.2.24
2021-01-12 0.2.21
2020-11-19 0.2.1
2020-06-03 0.2.0
2020-01-09 0.1.19
2019-06-28 0.1.17

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