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PESTO - Parameter Estimation Optimised, with APSIM Coupling

High-performance parameter estimation, uncertainty quantification, and inverse modelling toolkit built on modernised PEST++ algorithms. Provides native R interfaces to iterative ensemble smoothers (IES), Gauss-Levenberg-Marquardt (GLM) solvers, global sensitivity analysis, and multi-objective optimisation under uncertainty. Includes surrogate-accelerated IES via Gaussian Process and Random Fourier Features, adaptive SVD backends (randomised SVD, LAPACK, Eigen), and convergence-aware adaptive ensemble sizing. Designed for large-scale environmental, hydrological, and agricultural models with support for highly-parameterised problems (>100,000 parameters). The toolkit is model-independent -- any R function can serve as the forward model -- with first-class in-process coupling to the APSIM agricultural-systems model via the 'apsimx' package as its flagship simulator partner.

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openblascpp

5.02 score 1 dependents 11 scripts

kernR - Kernel-Based Causal Distributional Testing

Kernel-based hypothesis tests for causal inference and distributional treatment effects. Implements backdoor-adjusted HSIC (bd-HSIC) for testing causal association, and doubly robust kernel statistics (DR-DATE, DR-DETT) for testing distributional treatment effects beyond mean shifts. Supports binary, continuous, and mixed treatments, hierarchical/nested data structures, and scales to large datasets via Nystrom approximation and random Fourier features. This package depends on 'PESTO' (GPL (>= 3)); kernR's own sources are released under the MIT licence, but the installed combination with 'PESTO' is a combined work subject to the terms of the GPL (>= 3).

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openblascpp

4.59 score

proxymix - KL-Optimal Gaussian Mixture Proxies for Arbitrary Target Densities

Fits multivariate Gaussian-mixture proxies that are Kullback-Leibler optimal to user-supplied target densities on real Euclidean space. Three fitting regimes are unified under one verb: (i) closed-form moment matching for a single component, (ii) classical expectation-maximisation when independent samples are available, and (iii) importance-sampled KLD-EM when the target can be evaluated point-wise but not (cheaply) sampled. Closed-form Gaussian-mixture operators (density, sampling, marginalisation, conditioning, divergence) round out the toolkit. The conditioning operator drives multiple imputation of data missing at random, covering the multimodal and heteroscedastic cases a single-Gaussian model cannot represent. Implements the regime hierarchy of Hoek and Elliott (2024) <doi:10.1080/07362994.2024.2372605>.

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4.56 score

masque - Structurally Faithful Development Surrogates for Tabular Data

Turns a confidential tabular dataset - a single table, a folder of files, or a multi-sheet workbook - into a structurally faithful synthetic clone suitable for pipeline development. Experimental-design allocation and the NA pattern are preserved exactly; categorical design, treatment, and covariate vocabularies can be reversibly aliased; outcome and numeric-covariate values are re-simulated via a Gaussian copula that preserves the global covariance structure. Columns shared across tables are aliased consistently so the synthetic tables still join. A private 'recipe' object round-trips a pipeline written against the synthetic clone onto the original data. Not a differential-privacy or anonymisation tool: outputs are development surrogates, not public-release-safe artefacts.

Last updated

4.02 score

picMort - Reproducible Pediatric ICU Mortality Benchmark on PIC V1.1.0

Tools and pipelines for a calibration-first pediatric ICU mortality benchmark on the open-access PIC v1.1.0 database. Authoritative cohort specification, T+24h prediction-window feature extraction, PIM3 reconstruction, calibration and decision-curve evaluation. The package is the methodological substrate for the companion manuscript, illustrated end to end in a worked vignette.

Last updated

3.48 score