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vectra - Columnar Query Engine for Larger-than-RAM Data

A minimal columnar query engine with lazy execution on datasets larger than RAM. Provides 'dplyr'-like verbs (filter(), select(), mutate(), group_by(), summarise(), joins, window functions) and common aggregations (n(), sum(), mean(), min(), max(), sd(), first(), last()) backed by a pure C11 pull-based execution engine and a custom on-disk format ('.vtr'). Reads and writes 'GeoTIFF' (including tiled and 'BigTIFF' layouts) and a tiled raster format ('.vec') with overview pyramids and time cubes for larger-than-RAM raster data. Streams vector operations (spatial transforms, point-in-polygon and nearest-feature joins including a two-sided grid-partitioned join, select-by-location, clip, erase, dissolve, 'rasterization', 'polygonization', and contouring) through 'sf', and runs raster operations (zonal statistics, focal windows, terrain derivatives, resample or 'reproject' warp, polygon masking, map algebra, and 'mosaicking') in native C or over the tiled '.vec' format, one batch or tile at a time for data larger than RAM.

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ccolumnardata-analysislarge-dataquery-engineopenmp

8.89 score 9 stars 3 dependents 53 scripts 835 downloads

tulpa - Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

A general-purpose engine for fitting Bayesian hierarchical models with spatial fields, temporal effects, spatially varying coefficients, and multiple inference backends. Scalable spatial structure includes Hilbert space approximate Gaussian processes (HSGP; Riutort-Mayol et al. 2023 <doi:10.1007/s11222-022-10167-2>), nearest-neighbor Gaussian processes (NNGP; Datta et al. 2016 <doi:10.1080/01621459.2015.1044091>), intrinsic conditional autoregressive models (ICAR; Besag, York, and Mollie 1991 <doi:10.1007/BF00116466>), the reparameterized Besag-York-Mollie model (BYM2; Riebler et al. 2016 <doi:10.1177/0962280216660421>), and stochastic partial differential equation fields (SPDE; Lindgren, Rue, and Lindstrom 2011 <doi:10.1111/j.1467-9868.2011.00777.x>). Temporal structure covers random walks, autoregressive processes, and Gaussian processes. Inference is tiered by correctness guarantee: exact Hamiltonian Monte Carlo with the No-U-Turn sampler, Laplace and nested Laplace approximations with hyperparameter integration (Rue, Martino, and Chopin 2009 <doi:10.1111/j.1467-9868.2008.00700.x>), and variational inference. Model-specific packages plug observation likelihoods into the engine through a templated C++ callback interface.

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bayesianhierarchical-modelslaplace-approximationmcmcspatialcppopenmp

7.90 score 2 dependents 78 scripts 657 downloads

taxify - Offline Taxonomic Name Matching Against Darwin Core Backbones

Matches taxonomic names against local copies of published backbones ('Catalogue of Life', 'GBIF Backbone Taxonomy', 'ITIS', 'World Flora Online', 'WoRMS', 'NCBI Taxonomy', 'Open Tree of Life', 'Euro+Med', 'Species Fungorum', 'AlgaeBase', 'FishBase', 'SeaLifeBase', 'Reptile Database', 'LCVP', 'WCVP', 'Mammal Diversity Database', 'AviList', 'LPSN') and two vegetation classifications, and resolves each name to its accepted name. It performs the name-resolution step of 'taxize' and 'WorldFlora', run offline against these files. Names are cleaned of authors and qualifiers, matched exactly and then by edit distance within their genus, and followed from synonym to accepted name, with the backbone version recorded on every row. Published trait, status and distribution datasets are joined on the accepted name, and so is a table the user supplies. Matching runs in the 'vectra' column-store engine.

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biodiversitydarwin-coreecologyfuzzy-matchinggbifname-matchingnomenclatureofflinespecies-namessynonymstaxonomy

7.66 score 2 stars 1 dependents 49 scripts 511 downloads

couplr - Optimal Pairing and Matching via Linear Assignment

Solves optimal pairing and matching problems using linear assignment algorithms. Provides implementations of the Hungarian method (Kuhn 1955) <doi:10.1002/nav.3800020109>, Jonker-Volgenant shortest path algorithm (Jonker and Volgenant 1987) <doi:10.1007/BF02278710>, Auction algorithm (Bertsekas 1988) <doi:10.1007/BF02186476>, cost-scaling (Goldberg and Kennedy 1995) <doi:10.1007/BF01585996>, scaling algorithms (Gabow and Tarjan 1989) <doi:10.1137/0218069>, push-relabel (Goldberg and Tarjan 1988) <doi:10.1145/48014.61051>, and Sinkhorn entropy-regularized transport (Cuturi 2013) <doi:10.48550/arxiv.1306.0895>. Designed for matching plots, sites, samples, or any pairwise optimization problem. Supports rectangular matrices, forbidden assignments, data frame inputs, batch solving, k-best solutions, and pixel-level image morphing for visualization. Includes automatic preprocessing with variable health checks, multiple scaling methods (standardized, range, robust), greedy matching algorithms, and comprehensive balance diagnostics for assessing match quality using standardized differences and distribution comparisons.

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bipartite-matchinglinear-assignmentoptimizationcpp

6.90 score 2 stars 43 scripts 713 downloads

hexify - Equal-Area Hex Grids on the Snyder ISEA Icosahedron

Provides functions to build and use hexagonal discrete global grids using the Icosahedral Snyder Equal Area ('ISEA') projection (Snyder 1992 <doi:10.3138/27H7-8K88-4882-1752>) and the 'H3' hierarchical hexagonal system (Uber Technologies). Implements the 'ISEA' discrete global grid system (Sahr, White and Kimerling 2003 <doi:10.1559/152304003100011090>) and the 'Hex9' octahedral grid (Griffin 2026 <arXiv:2608.00022>). Includes a fast C++ core for 'ISEA' projection and aperture quantization, an included 'H3' v4.4.1 C library for native 'H3' grid operations, and 'sf'/'terra'-compatible R wrappers for grid generation and coordinate assignment. Output is compatible with 'dggridR' for interoperability.

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dggsdiscrete-global-gridequal-areageospatialgishexagonal-gridicosahedral-gridicosahedroniseaspatialspatial-analysiscpp

6.66 score 1 stars 1 dependents 34 scripts 290 downloads

corrselect - Correlation-Based and Model-Based Predictor Pruning

Provides functions for predictor pruning using association-based and model-based approaches. Includes corrPrune() for fast correlation-based pruning, modelPrune() for regression pruning on the variance inflation factor (VIF) and the condition number, and exact graph-theoretic algorithms (Eppstein–Löffler–Strash, Bron–Kerbosch) for exhaustive subset enumeration. Supports linear models, generalized linear models (GLMs), and mixed models ('lme4', 'glmmTMB').

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correlationenumerationfeature-selection-glmgraph-algorithmsmachine-learningmixed-modelsmulticollinearityregressionstatisticsvariable-selectionvifcpp

6.43 score 3 stars 15 scripts 392 downloads

ggguides - Simplified Legend and Guide Alignment for 'ggplot2'

Provides one-liner functions for common legend and guide operations in 'ggplot2'. Simplifies legend positioning, styling, wrapping, and collection across multi-panel plots created with 'patchwork' or 'cowplot'.

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data-visualizationdatavizggplot2ggplot2-extensionlegendpatchwork

6.35 score 5 stars 12 scripts 357 downloads

tulpaMesh - Constrained Delaunay Triangulation Meshes for Spatial 'SPDE' Models

Generate constrained Delaunay triangulation meshes for use with stochastic partial differential equation (SPDE) spatial models (Lindgren, Rue and Lindstroem 2011 <doi:10.1111/j.1467-9868.2011.00777.x>). Provides automatic mesh generation from point coordinates with boundary constraints, Ruppert refinement for mesh quality, finite element method (FEM) matrix assembly (mass, stiffness, projection), barrier models, spherical meshes via icosahedral subdivision, and metric graph meshes for network geometries. Built on the 'CDT' header-only C++ library (Amirkhanov 2024 <https://github.com/artem-ogre/CDT>). Designed as the mesh backend for the 'tulpa' Bayesian hierarchical modelling engine but usable standalone for any spatial triangulation task.

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delaunayecologyfemgeostatisticsmeshspatialspdecpp

6.17 score 1 stars 3 dependents 9 scripts 645 downloads

tulpaObs - Bayesian Occupancy, Abundance and Detection Models via 'tulpa'

Fits Bayesian models for species observed with imperfect detection: single-season and dynamic site occupancy (MacKenzie et al. (2002) <doi:10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2>), N-mixture abundance from replicated counts (Royle (2004) <doi:10.1111/j.0006-341X.2004.00142.x>), distance sampling, removal and double-observer counts, false-positive and time-to-detection occupancy, integrated models combining several data sources, multi-species and joint species distribution models, and a hurdle model for vegetation cover. Linear predictors take fixed, random, spatial and temporal effects. Each model supplies its observation likelihood to the 'tulpa' engine, which fits it by Laplace approximation, by nested Laplace integration over the hyperparameters, or by Hamiltonian Monte Carlo with the No-U-Turn Sampler (NUTS) using automatic differentiation. Simulation, prediction and diagnostic functions (posterior predictive checks, leave-one-out cross-validation, simulation-based calibration) accompany each model.

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bayesianecologyoccupancyspatialcppopenmp

6.01 score 57 scripts

BORG - Bounded Outcome Risk Guard for Model Evaluation

Comprehensive toolkit for valid spatial, temporal, and grouped model evaluation. Automatically detects data dependencies (spatial autocorrelation, temporal structure, clustered observations), generates appropriate cross-validation schemes (spatial blocking, checkerboard, hexagonal, KNNDM, environmental blocking, leave-location-out, purged CV), and validates evaluation pipelines for leakage. Includes area of applicability (AOA) assessment following Meyer & Pebesma (2021) <doi:10.1111/2041-210X.13650>, forward feature selection with blocked CV, spatial thinning, block-permutation variable importance, extrapolation detection, and interactive visualizations. Integrates with 'tidymodels', 'caret', 'mlr3', 'ENMeval', and 'biomod2'. Based on evaluation principles described in Roberts et al. (2017) <doi:10.1111/ecog.02881>, Kaufman et al. (2012) <doi:10.1145/2382577.2382579>, Kapoor & Narayanan (2023) <doi:10.1016/j.patter.2023.100804>, and Linnenbrink et al. (2024) <doi:10.5194/gmd-17-5897-2024>.

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dependencemodel-evaluationriskvalidationcpp

6.01 score 15 scripts 413 downloads

joinspy - Diagnostic Tools for Data Frame Joins

Provides diagnostic tools for understanding and debugging data frame joins. Analyzes key columns before joining to detect duplicates, mismatches, encoding issues, and other common problems. Explains unexpected row count changes and provides safe join wrappers with cardinality enforcement. Concepts and diagnostics build on tidy data principles as described in 'Wickham' (2014) <doi:10.18637/jss.v059.i10>.

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data-wranglingdiagnosticsdyplrjoins

6.00 score 4 stars 9 scripts 245 downloads

restrictR - Composable Runtime Contracts

Build reusable validators from small building blocks using the base pipe operator. Define runtime contracts once with restrict() and enforce them anywhere in code. Validators compose naturally, support dependent rules via formulas, and produce clear, path-aware error messages. No domain-specific language, no operator overloading, just idiomatic R.

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contractsruntime-checksruntime-contractsvalidation

5.45 score 7 stars 6 scripts 308 downloads

getaca - Reproducible External Data Dependencies

Declares, retrieves, verifies, tracks and actively manages external data dependencies too large or too fast-moving to ship inside a package. Resources are identified by package, name and version, pinned to a Secure Hash Algorithm (SHA-256) checksum, and resolved through an explicit policy so that the same installed package always resolves the same bytes. A registry served from a remote host may be signed with Ed25519 and verified against a key the declaring package ships, so the declaration and the key that vouches for it arrive by different routes. Hashing follows National Institute of Standards and Technology (2015) "Secure Hash Standard" <doi:10.6028/NIST.FIPS.180-4>; signing follows Bernstein, Duif, Lange, Schwabe and Yang (2012) "High-Speed High-Security Signatures" <doi:10.1007/s13389-012-0027-1> and Josefsson and Liusvaara (2017) "Edwards-Curve Digital Signature Algorithm (EdDSA)" <doi:10.17487/RFC8032>. Designed for reproducible offline use and graceful behaviour during package checks.

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cachingchecksumdata-managementreproducibility

5.38 score 1 stars 12 scripts 259 downloads

keyed - Explicit Key Assumptions for Flat-File Data

Helps make implicit data assumptions explicit by attaching keys to flat-file data that error when those assumptions are violated. Designed for CSV-first workflows without database infrastructure or version control. Provides key definition, assumption checks, join diagnostics, and automatic drift detection via watched data frames that snapshot before each transformation and report cell-level changes.

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csvdata-integritydata-qualitydata-validationdata-wranglingflat-filetidyverse

5.30 score 2 stars 8 scripts 310 downloads

areaOfEffect - Classify Points by Distance to Polygon Boundaries

Classifies spatial points relative to polygon boundaries, labeling each point as "core" (inside), "halo" (in a buffer zone), or pruning it (outside both). Handles projection, buffering, and point-in-polygon operations automatically. The default buffer produces equal core and halo areas, providing a scale-independent definition of "near the boundary." An optional mask clips the buffer to relevant areas such as coastlines.

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5.12 score 1 stars 12 scripts 348 downloads

tulpaRatio - Bayesian Hierarchical Models for Ratios, Rates, and Proportions

An opinionated framework for Bayesian hierarchical modelling of ratios, rates, and proportions. Ratios are treated as derived quantities, with all inference performed on the latent processes generating the numerator and denominator. Supports shared random effects, latent factors for unmeasured confounders, spatial structure ('CAR'/'BYM2'/'GP'), spatiotemporal interaction (Knorr-Held Type I-IV), zero-inflation variants, and full uncertainty propagation to ratio posteriors. Provides multiple inference backends including custom 'HMC'/'NUTS' (default), 'Laplace' approximation, and 'Polya-Gamma' Gibbs sampling.

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bayesianecologyhierarchical-modelsproportionsratioscppopenmp

4.95 score 11 scripts

topocast - Moving-Window Regression Downscaling of Raster Data

Downscales coarse-resolution raster data to a finer grid by fitting local linear regressions of a response, such as a climate variable, on one or more fine-resolution predictors, such as elevation and other terrain indices, within a moving window. Regression coefficients are estimated for every cell using summed-area tables, so the cost is independent of the window size, then resampled to the target resolution and applied to the fine-resolution predictors. Multiplicative and additive anomaly application downscale time series relative to a baseline climatology, following the regression-on-elevation approach used for high-resolution climate surfaces described in Karger et al. (2017) <doi:10.1038/sdata.2017.122>.

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climatedownscalinggeospatialrasterterraopenblascppopenmp

4.78 score 8 scripts 213 downloads

texanshootR - Reproducible Audit Trails for Indefensible Research

Provides a structured, terminal-first interface for exploratory model search, including transformation grids, predictor-subset enumeration, interaction screening, principled- sounding sample restrictions, outcome engineering, and model-form escalation (polynomial / spline wraps, robust M-estimation, generalized linear model (GLM) family swaps, random-intercept lifts). Persistent run history, achievement tracking, and reportable output generators (manuscript, presentation, funding letter, graphical abstract, reviewer response) are included.

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exploratory-analysislinear-modelsroguelikecpp

4.70 score 8 scripts 303 downloads

timesift - Learn Predictive Representations of Time-Varying Data

Fits and compares representations of time-varying data against a prediction target. Given a table of targets and a table of time-stamped series belonging to them, it builds each candidate representation, from the record unreduced through a calendar grain such as a week or a month to a lookback anchored on each target, fits the requested learners on each, scores every candidate on one set of held-out folds, and stacks the out-of-fold predictions into an ensemble. Calendar-aware binning keeps a bin a real week or month rather than a fixed block of hours. Learners, response heads and metrics are registered rather than hard-coded, so adding one is a registration and not a fork of the fitting code. The penalised baseline is an elastic net fitted by cyclic coordinate descent along a warm-started path, following Friedman, Hastie and Tibshirani (2010) <doi:10.18637/jss.v033.i01>. The shipped default is presence-absence with a joint multi-label head scored by the true skill statistic of Allouche, Tsoar and Kadmon (2006) <doi:10.1111/j.1365-2664.2006.01214.x>, the setting used for species distribution modelling from microclimate loggers.

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microclimatepythonspecies-distribution-modellingtime-seriescpp

4.59 score 13 scripts 195 downloads

OccurrenceCleaner - Fast Quality Checks for Species Occurrence Records

An extension of 'CoordinateCleaner' (Zizka et al. 2019 <doi:10.1111/2041-210X.13152>) tailored to large-scale species occurrence data. Flags common problems in occurrence records, such as coordinates at country centroids, capitals or biodiversity institutions, zero or equal coordinates, records at sea, in the wrong country or outside the species range, duplicates, outliers within a species, and datasets with converted or gridded coordinates, with the test definitions and default buffers of 'CoordinateCleaner' apart from documented differences. It also reads the quality warnings GBIF attaches to each record, the basis of record and the establishment means, and checks records at sea or on land against the habitat of their genus from the Interim Register of Marine and Nonmarine Genera. Records are flagged and kept, and users choose afterwards which flags to filter on. Data frames of a few million records are flagged in seconds, and GBIF snapshots of billions of records are processed by reading Parquet files in chunks.

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biodiversitycoordinatecleanerdata-cleaninggbifoccurrence-datarcppcppopenmp

4.41 score 26 scripts

taxifydb - Build Backbones and Enrichments for the 'taxify' Package

Build pipeline for the 'taxify' package. Downloads raw source data from official providers (WFO, COL, GBIF, ITIS, NCBI Taxonomy, Open Tree of Life, WoRMS, Euro+Med PlantBase, Index Fungorum, AlgaeBase, FishBase, SeaLifeBase, the Reptile Database, the World Checklist of Vascular Plants, and the Leipzig Catalogue of Vascular Plants) and a wide set of trait and conservation datasets, normalizes to a unified Darwin Core-like schema, and writes pre-compiled '.vtr' files that the 'taxify' runtime consumes. Separates build-time concerns (network access, parsing, schema normalization) from runtime concerns so that 'taxify' itself stays lean.

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biodiversitydarwin-coretaxonomy

3.48 score 3 stars 3 scripts