CRAN updates: nhppp #rstats

CRAN updates: germinationmetrics ISAR localScore mcount PGRdup taxodist xpose.xtras #rstats

CRAN: Package zoteR Connects R to 'Zotero' through the 'Better BibTeX' for 'Zotero' connector &lt;<a href="https://retorque.re/zotero-better-bibtex/" target="_top">https://retorque.re/zotero-better-bibtex/</a>&gt;. Provides functions to insert in-text citations and bibliography entries directly into documents, detect citations already present in R Markdown and Quarto files, and synchronise bibliography files. Includes an 'RStudio addin' for interactive use.

New CRAN package zoteR with initial version 1.0.0
#rstats
https://cran.r-project.org/package=zoteR

CRAN: Package xtife Implements the interactive fixed effects ('IFE') panel estimator of Bai (2009) &lt;<a href="https://doi.org/10.3982%2FECTA6135" target="_top">doi:10.3982/ECTA6135</a>&gt; with analytical standard errors ('homoskedastic', 'HC1' robust, and cluster-robust by unit). Supports asymptotic bias correction for large panels (Bai 2009) and a dynamic extension for predetermined regressors (Moon and Weidner 2017 &lt;<a href="https://doi.org/10.1017%2FS0266466615000328" target="_top">doi:10.1017/S0266466615000328</a>&gt;). Includes information-criterion-based factor number selection (Bai and Ng 2002 &lt;<a href="https://doi.org/10.1111%2F1468-0262.00273" target="_top">doi:10.1111/1468-0262.00273</a>&gt;). All computations use base R only with no external dependencies.

New CRAN package xtife with initial version 0.1.3
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https://cran.r-project.org/package=xtife

CRAN: Package symbolicr Find non-linear formulas that fits your input data. You can systematically explore and memorize the possible formulas and it's cross-validation performance, in an incremental fashion. Three main interoperable search functions are available: 1) random.search() performs a random exploration, 2) genetic.search() employs a genetic optimization algorithm, 3) comb.search() combines best results of the first two. For more details see Tomasoni et al. (2026) &lt;<a href="https://doi.org/10.1208%2Fs12248-026-01232-z" target="_top">doi:10.1208/s12248-026-01232-z</a>&gt;.

New CRAN package symbolicr with initial version 1.0.0
#rstats
https://cran.r-project.org/package=symbolicr

CRAN: Package rmet Automates the download and processing of historical weather data from the Brazilian National Institute of Meteorology (INMET). It resolves formatting inconsistencies in raw CSV files across different years, removes structural artifacts, standardizes column names, converts timestamps to local Brazilian time zones, and outputs tidy data frames ready for analysis. Data are retrieved from &lt;<a href="https://portal.inmet.gov.br/dadoshistoricos" target="_top">https://portal.inmet.gov.br/dadoshistoricos</a>&gt;.

New CRAN package rmet with initial version 0.1.0
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https://cran.r-project.org/package=rmet

CRAN: Package ordered Bindings, methods, and tuners for using ordinal classification models with the 'parsnip' and 'dials' packages. These include the regularized elastic net ordinal regression of Wurm, Hanlon, and Rathouz (2021) &lt;<a href="https://doi.org/10.18637%2Fjss.v099.i06" target="_top">doi:10.18637/jss.v099.i06</a>&gt; in 'ordinalNet', the ordinal classification trees of Galimberti, Soffritti, and Di Maso (2012) &lt;<a href="https://doi.org/10.18637%2Fjss.v047.i10" target="_top">doi:10.18637/jss.v047.i10</a>&gt; in 'rpartScore', and the latent variable ordinal forests of Hornung (2020) &lt;<a href="https://doi.org/10.1007%2Fs00357-018-9302-x" target="_top">doi:10.1007/s00357-018-9302-x</a>&gt; in 'ordinalForest'.

New CRAN package ordered with initial version 0.1.0
#rstats
https://cran.r-project.org/package=ordered

CRAN: Package lorbridge Provides a unified analytical workflow that bridges conventional binary and multinomial logistic regression with singly-ordered (SONSCA) and doubly-ordered (DONSCA) nonsymmetric correspondence analysis. Log-odds ratios (LORs) from logistic regression are re-expressed as cosine theta estimates and closeness-of-concordance measures (CCMs) &ndash; including Yule's Q, Yule's Y, and r_meta &ndash; on the familiar [-1, +1] scale introduced by Kim and Grochowalski (2019) &lt;<a href="https://doi.org/10.3758%2Fs13428-018-1161-1" target="_top">doi:10.3758/s13428-018-1161-1</a>&gt;. Bootstrap confidence intervals for cosine theta are provided throughout. The package is intended to help clinical and medical researchers interpret association strength from logistic regression in an intuitive, correlation-like metric, and to connect conventional regression results with geometric correspondence analysis visualisations.

New CRAN package lorbridge with initial version 0.1.0
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https://cran.r-project.org/package=lorbridge

CRAN: Package funcml A compact and explicit machine learning framework for supervised learning, resampling-based evaluation, hyperparameter tuning, learner comparison, interpretation, and plug-in g-computation. The package uses standard formulas for model specification and provides stable S3 interfaces for fitting, evaluation, tuning, interpretation, and causal estimation across a learner registry with multiple backend engines. Implemented interpretation methods build on established approaches such as permutation-based variable importance, partial dependence, individual conditional expectation, accumulated local effects, SHAP, and LIME; see Friedman (2001) &lt;<a href="https://doi.org/10.1214%2Faos%2F1013203451" target="_top">doi:10.1214/aos/1013203451</a>&gt;, Goldstein et al. (2015) &lt;<a href="https://doi.org/10.1080%2F10618600.2014.907095" target="_top">doi:10.1080/10618600.2014.907095</a>&gt;, Apley and Zhu (2020) &lt;<a href="https://doi.org/10.1111%2Frssb.12377" target="_top">doi:10.1111/rssb.12377</a>&gt;, Lundberg and Lee (2017) &lt;<a href="https://doi.org/10.48550%2FarXiv.1705.07874" target="_top">doi:10.48550/arXiv.1705.07874</a>&gt;, and Ribeiro et al. (2016) &lt;<a href="https://doi.org/10.48550%2FarXiv.1602.04938" target="_top">doi:10.48550/arXiv.1602.04938</a>&gt;. The framework is intentionally opinionated: preprocessing is expected to occur outside the modeling step, and the API emphasizes explicit inputs, consistent object contracts, and compact interfaces rather than feature-by-feature competition with larger machine learning ecosystems.

New CRAN package funcml with initial version 0.7.1
#rstats
https://cran.r-project.org/package=funcml

CRAN: Package debrief Provides text-based summaries and analysis tools for 'profvis' profiling output. Designed for terminal workflows and artificial intelligence (AI) agent consumption, offering views including hotspot analysis, call trees, source context, caller/callee relationships, and memory allocation breakdowns.

New CRAN package debrief with initial version 0.1.0
#rstats
https://cran.r-project.org/package=debrief

CRAN readmissions: ordinalClust sundialr #rstats

CRAN: Package ZonationR An interface to 'Zonation' software, enabling users to run spatial conservation prioritization workflows in 'R'. It streamlines input preparation, execution, and post-processing, while supporting reproducibility and lowering the entry barrier for learning, teaching, and research in conservation planning. The methods implemented in 'Zonation' are described in Moilanen et al. (2022) &lt;<a href="https://doi.org/10.1111%2F2041-210X.13819" target="_top">doi:10.1111/2041-210X.13819</a>&gt;.

New CRAN package ZonationR with initial version 1.0.1
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https://cran.r-project.org/package=ZonationR

CRAN: Package XSRecencyX Tools for estimating HIV incidence using cross-sectional recency testing data, adjusting for internal and external target populations and supporting subtype-specific parameters. The statistical methodology implemented builds on the framework described in Wang, Duerr, and Gao(2025) &lt;<a href="https://doi.org/10.1002%2Fsim.70216" target="_top">doi:10.1002/sim.70216</a>&gt;.

New CRAN package XSRecencyX with initial version 0.1.0
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https://cran.r-project.org/package=XSRecencyX

CRAN: Package wdsmatch Implements weighted double score matching (WDSM) for estimating population-level causal effects from complex survey data. Combines propensity scores and prognostic scores with survey design weights for matching, survey-weighted imputation within match sets, and Hajek normalization to target the population average treatment effect (PATE) and the population average treatment effect on the treated (PATT). Supports both retrospective (treatment-dependent) and prospective (treatment-independent) sampling designs. Achieves double robustness: consistent estimation when either the propensity score or prognostic score model is correctly specified. Provides polynomial sieve bias correction and linearization-based multinomial bootstrap variance estimation that preserves the survey-weighted matching structure without re-matching. Methods are described in Zeng, Tong, Tong, Lu, Mukherjee, and Li (2026, under review) "Where to weight? Estimating population causal effects with weighted double score matching in complex surveys".

New CRAN package wdsmatch with initial version 0.1.1
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https://cran.r-project.org/package=wdsmatch

CRAN: Package wdiexplorer Provides a workflow for exploring World Development Indicators (WDI) country-level panel data. It downloads WDI data using the 'WDI' package and computes diagnostic indices that capture the temporal behaviour of the data by incorporating the grouping structure of the data. The set of diagnostic indices implemented includes variation features, trend and shape features, and sequential temporal features. This method is described in Akinfenwa, Cahill, and Hurley (2025) "'wdiexplorer': An R package Designed for Exploratory Analysis of World Development Indicators (WDI) Data" &lt;<a href="https://doi.org/10.48550%2FarXiv.2511.07027" target="_top">doi:10.48550/arXiv.2511.07027</a>&gt;. We adapt the clustering diagnostics and visualisation methodology described in Rousseeuw (1987) &lt;<a href="https://doi.org/10.1016%2F0377-0427%2887%2990125-7" target="_top">doi:10.1016/0377-0427(87)90125-7</a>&gt; and selected time series features from Hyndman and Athanasopoulos (2021) "Forecasting: Principles and Practice" &lt;<a href="https://otexts.com/fpp3/" target="_top">https://otexts.com/fpp3/</a>&gt;.

New CRAN package wdiexplorer with initial version 0.1.2
#rstats
https://cran.r-project.org/package=wdiexplorer

CRAN: Package wcc Calculates Windowed Cross Correlation for pairs of time series. Provides support for surrogate analysis for nonparametric test of significance. Calculates aggregate statistics over a range of parameter values. Plots the results as Windowed Cross Correlation plots and heat maps. The method is described in "Boker, S. M., Rotondo, J. L., Xu, M., &amp; King, K. (2002). Windowed cross-correlation and peak picking for the analysis of variability in the association between behavioral time series. Psychological Methods, 7(3), 338."

New CRAN package wcc with initial version 0.3.1
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https://cran.r-project.org/package=wcc

CRAN: Package vectra 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').

New CRAN package vectra with initial version 0.5.1
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https://cran.r-project.org/package=vectra

CRAN: Package umweltapir Provides an R-based access to the datasets including their resources from the portal &lt;<a href="https://umwelt.info" target="_top">https://umwelt.info</a>&gt;. The package allows for an easy integration of those datasets into your R-based workflows. The functionality of the package mirrors the web-based access as provided at &lt;<a href="https://umwelt.info" target="_top">https://umwelt.info</a>&gt;. You can use the same queries and get the same datasets by accessing our API.

New CRAN package umweltapir with initial version 0.1.0
#rstats
https://cran.r-project.org/package=umweltapir

CRAN: Package truncProxy Implements proximal weighting estimators for the expectation of an arbitrarily transformed event time under dependent left truncation, with optional inverse probability of censoring weighting to handle right censoring. The methods leverage proxy variables to handle dependent left truncation in settings where dependence-inducing factors are not fully observed.

New CRAN package truncProxy with initial version 0.1.0
#rstats
https://cran.r-project.org/package=truncProxy

CRAN: Package talib Interface to the 'TA-Lib' (Technical Analysis Library) C library, providing access to 150+ indicators (e.g. Average Directional Movement Index (ADX), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator, Bollinger Bands), candlestick pattern recognition, and rolling-window utilities. Core computations are implemented in C for fast Open-High-Low-Close-Volume (OHLCV) time-series feature engineering and rule-based signal generation, with optional interactive visualization via 'plotly'.

New CRAN package talib with initial version 0.9-0
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https://cran.r-project.org/package=talib

CRAN: Package srpi Flexible implementation of the Standardized Ranking Performance Index (sRPI) for model selection based on multiple evaluation criteria. The package combines multiple statistical measures into a single index to provide an objective and robust ranking of models across calibration, validation, and combined scenarios. It supports evaluation of statistical, machine learning, and other predictive models using user-defined performance criteria. For more details see Aschonitis et al. (2019) &lt;<a href="https://doi.org/10.1016%2Fj.envsoft.2019.01.005" target="_top">doi:10.1016/j.envsoft.2019.01.005</a>&gt; and Singh et al. (2023) &lt;<a href="https://doi.org/10.1016%2Fj.ecoinf.2022.101933" target="_top">doi:10.1016/j.ecoinf.2022.101933</a>&gt;.

New CRAN package srpi with initial version 0.1.0
#rstats
https://cran.r-project.org/package=srpi

CRAN: Package spatialData Provides spatial datasets ready to use for ecological modelling and raster companion data for prediction: Neanderthal presence during the Last Interglacial (Benito et al. 2017 &lt;<a href="https://doi.org/10.1111%2Fjbi.12845" target="_top">doi:10.1111/jbi.12845</a>&gt;); Plant diversity metrics for the World's Ecoregions (Maestre et al. 2021 &lt;<a href="https://doi.org/10.1111%2Fnph.17398" target="_top">doi:10.1111/nph.17398</a>&gt;); tree richness across the Americas (Benito et al. 2013 &lt;<a href="https://doi.org/10.1111%2F2041-210X.12022" target="_top">doi:10.1111/2041-210X.12022</a>&gt;); plant communities from the Sierra Nevada (Spain) with future climate scenarios (Benito et al. 2013 &lt;<a href="https://doi.org/10.1111%2F2041-210X.12022" target="_top">doi:10.1111/2041-210X.12022</a>&gt;); butterfly-plant interaction data from Sierra Nevada (Spain) (Benito et al. 2011 &lt;<a href="https://doi.org/10.1007%2Fs10584-010-0015-3" target="_top">doi:10.1007/s10584-010-0015-3</a>&gt;); plant species occurrences in Andalusia (Spain) (Benito et al. 2014 &lt;<a href="https://doi.org/10.1111%2Fddi.12148" target="_top">doi:10.1111/ddi.12148</a>&gt;); presence of the plant Linaria nigricans and greenhouses (Benito et al. 2009 &lt;<a href="https://doi.org/10.1007%2Fs10531-009-9604-8" target="_top">doi:10.1007/s10531-009-9604-8</a>&gt;); global NDVI and environmental predictors, and European oak species occurrences. All datasets include pre-processed environmental predictors ready for statistical modelling.

New CRAN package spatialData with initial version 1.0.0
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https://cran.r-project.org/package=spatialData

CRAN: Package sfReapportion A port of the 'spReapportion' package, using Simple Features in order to lose the dependencies to the retired 'maptools' and 'rgeos' packages.

New CRAN package sfReapportion with initial version 0.2.0
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https://cran.r-project.org/package=sfReapportion

CRAN: Package QuickExplore A 'Shiny' application that provides nice interface for browsing, exploring, summarising, and converting datasets stored in 'SAS' (.sas7bdat, .xpt), CSV (.csv), and 'R' (.rds) formats. Users can register multiple directory-based libraries, interactively filter data using 'dplyr' expressions, inspect per-variable statistics, and export datasets to Excel, JSON, CSV, 'R' data, or 'SAS' transport formats.

New CRAN package QuickExplore with initial version 0.1.0
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https://cran.r-project.org/package=QuickExplore

CRAN: Package orbitr A lightweight, fully vectorized N-body physics engine built for the R ecosystem. Simulate and visualize complex orbital mechanics, celestial trajectories, and gravitational interactions using tidy data principles. Features multiple numerical integration methods, including the energy-conserving velocity Verlet algorithm (Verlet (1967) &lt;<a href="https://doi.org/10.1103%2FPhysRev.159.98" target="_top">doi:10.1103/PhysRev.159.98</a>&gt;), to ensure highly stable orbital propagation. Gravitational N-body methods follow Aarseth (2003, ISBN:0-521-43272-3).

New CRAN package orbitr with initial version 0.2.0
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https://cran.r-project.org/package=orbitr

CRAN: Package MRIreduce Converts NIfTI format T1/FL neuroimages into structured, high-dimensional 2D data frames with a focus on region of interest (ROI) based processing. The package incorporates the partition algorithm, which offers a flexible framework for agglomerative partitioning based on the Direct-Measure-Reduce approach. This method ensures that each reduced variable maintains a user-specified minimum level of information while remaining interpretable, as each maps uniquely to one variable in the reduced dataset. The partition framework is described in Millstein et al. (2020) &lt;<a href="https://doi.org/10.1093%2Fbioinformatics%2Fbtz661" target="_top">doi:10.1093/bioinformatics/btz661</a>&gt;. The package allows customization in variable selection, measurement of information loss, and data reduction methods for neuroimaging analysis and machine learning workflows.

New CRAN package MRIreduce with initial version 1.0.0
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https://cran.r-project.org/package=MRIreduce

CRAN: Package mori Share R objects across processes on the same machine via a single copy in 'POSIX' shared memory (Linux, macOS) or a 'Win32' file mapping (Windows). Every process reads from the same physical pages through the R Alternative Representation ('ALTREP') framework, giving lazy, zero-copy access. Shared objects serialize compactly as their shared memory name rather than their full contents.

New CRAN package mori with initial version 0.1.0
#rstats
https://cran.r-project.org/package=mori

CRAN: Package leaf A unified framework for symbolic regression (SR) and multi-view symbolic regression (MvSR) designed for complex, nonlinear systems, with particular applicability to ecological datasets. The package implements a four-stage workflow: data subset generation, functional form discovery, numerical parameter optimization, and multi-objective evaluation. It provides a high-level formula-style interface that abstracts and extends multiple discovery engines: genetic programming (via PySR), Reinforcement Learning with Monte Carlo Tree Search (via RSRM), and exhaustive generalized linear model search. 'leaf' extends these methods by enabling multi-view discovery, where functional structures are shared across groups while parameters are fitted locally, and by supporting the enforcement of domain-specific constraints, such as sign consistency across groups. The framework automatically handles data normalization, link functions, and back-transformation, ensuring that discovered symbolic equations remain interpretable and valid on the original data scale. Implements methods following ongoing work by the authors (2026, in preparation).

New CRAN package leaf with initial version 0.1.0
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https://cran.r-project.org/package=leaf

CRAN: Package elcf4R Implements forecasting methods for individual electricity load curves, including Kernel Wavelet Functional (KWF), clustered KWF, Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), and Long Short-Term Memory (LSTM) models. Provides normalized dataset adapters for iFlex, StoreNet, Low Carbon London, and REFIT; download and read support for IDEAL and GX; explicit Python backend selection for TensorFlow-based LSTM fits; helpers for daily segmentation and rolling-origin benchmarking; and compact shipped example panels and benchmark-result datasets.

New CRAN package elcf4R with initial version 0.4.0
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https://cran.r-project.org/package=elcf4R

CRAN: Package DSIR Tools for global health data analysis, including a publication-ready 'ggplot2' theme, a 'flextable' defaults helper, a thin pie chart wrapper, built-in regional country-code datasets, and convenience clients for the World Health Organization Global Health Observatory (GHO) OData API &lt;<a href="https://ghoapi.azureedge.net/api/" target="_top">https://ghoapi.azureedge.net/api/</a>&gt; and the United Nations Sustainable Development Goals (SDG) API &lt;<a href="https://unstats.un.org/SDGAPI/swagger/" target="_top">https://unstats.un.org/SDGAPI/swagger/</a>&gt;.

New CRAN package DSIR with initial version 0.2.0
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https://cran.r-project.org/package=DSIR