This package provides tools for the processing, segmentation, and analysis of terrestrial laser scanning (TLS and MLS) forest point-cloud data. The package provides fast voxel-based processing, classification of point clouds into forest floor, understory, canopy, and woody components, and algorithms for single-tree analysis and structural characterization. Methods are designed to handle large and dense point-cloud datasets efficiently, supporting applications in forest structure assessment, connectivity analysis, and fire-risk evaluation. Input data are provided as .xyz', .txt', .las', or .laz point-cloud files. The circle-fitting routines used for diameter estimation are adapted, in base R, from the conicfit package (GPL-3) by Jose Gama, based on the original algorithms and code by Nikolai Chernov. For methodological details, see Ferrara and Arrizza (2025) <https://hdl.handle.net/20.500.14243/533471> and Ferrara et al. (2018) <doi:10.1016/j.agrformet.2018.04.008>.

Collective matrix factorization (CMF) finds joint low-rank representations for a collection of matrices with shared row or column entities. This code learns a variational Bayesian approximation for CMF, supporting multiple likelihood potentials and missing data, while identifying both factors shared by multiple matrices and factors private for each matrix. For further details on the method see Klami et al. (2014) <arXiv:1312.5921>. The package can also be used to learn Bayesian canonical correlation analysis (CCA) and group factor analysis (GFA) models, both of which are special cases of CMF. This is likely to be useful for people looking for CCA and GFA solutions supporting missing data and non-Gaussian likelihoods. See Klami et al. (2013) <https://research.cs.aalto.fi/pml/online-papers/klami13a.pdf> and Virtanen et al. (2012) <http://proceedings.mlr.press/v22/virtanen12.html> for details on Bayesian CCA and GFA, respectively.

General P-splines are non-uniform B-splines penalized by a general difference penalty, proposed by Li and Cao (2022) <arXiv:2201.06808>. Constructible on arbitrary knots, they extend the standard P-splines of Eilers and Marx (1996) <doi:10.1214/ss/1038425655>. They are also related to the O-splines of O'Sullivan (1986) <doi:10.1214/ss/1177013525> via a sandwich formula that links a general difference penalty to a derivative penalty. The package includes routines for setting up and handling difference and derivative penalties. It also fits P-splines and O-splines to (x, y) data (optionally weighted) for a grid of smoothing parameter values in the automatic search intervals of Li and Cao (2023) <doi:10.1007/s11222-022-10178-z>. It aims to facilitate other packages to implement P-splines or O-splines as a smoothing tool in their model estimation framework.

Fetch Australian Clean Energy Regulator data on carbon credits, safeguard mechanism facilities, renewable energy certificates, and greenhouse gas reporting. Provides tidy access to the Australian Carbon Credit Unit (ACCU) Scheme project register, Safeguard Mechanism baselines and covered emissions, Large-scale Renewable Energy Target (LRET) power station accreditations, Small-scale Renewable Energy Scheme (SRES) installation data, the National Greenhouse and Energy Reporting (NGER) scheme, and Quarterly Carbon Market Reports <https://cer.gov.au/markets/reports-and-data>. Includes a post-Chubb ACCU integrity layer (Chubb 2022 Independent Review), Safeguard reform handling (declining industry baselines from July 2023), National Greenhouse and Energy Reporting scope discipline (Scope 1 / Scope 2 market vs location / Climate Active), reconciliation against the Quarterly Carbon Market Report, and reproducibility helpers (snapshot pinning, SHA-256 cache integrity, session manifest, optional Zenodo deposit). Data is published by the Clean Energy Regulator under a Creative Commons Attribution 4.0 International licence.

This package implements the doubly robust distribution balancing weighting proposed by Katsumata (2024) <doi:10.1017/psrm.2024.23>, which improves the augmented inverse probability weighting (AIPW) by estimating propensity scores with estimating equations suitable for the pre-specified parameter of interest (e.g., the average treatment effects or the average treatment effects on the treated) and estimating outcome models with the estimated inverse probability weights. It also implements the covariate balancing propensity score proposed by Imai and Ratkovic (2014) <doi:10.1111/rssb.12027> and the entropy balancing weighting proposed by Hainmueller (2012) <doi:10.1093/pan/mpr025>, both of which use covariate balancing conditions in propensity score estimation. The point estimate of the parameter of interest and its uncertainty as well as coefficients for propensity score estimation and outcome regression are produced using the M-estimation. The same functions can be used to estimate average outcomes in missing outcome cases.

Index of Multiple Deprivation for UK nations at various geographical levels. In England, deprivation data is for Lower Layer Super Output Areas, Middle Layer Super Output Areas, Wards, and Local Authorities based on data from <https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019>. In Wales, deprivation data is for Lower Layer Super Output Areas, Middle Layer Super Output Areas, Wards, and Local Authorities based on data from <https://gov.wales/welsh-index-multiple-deprivation-full-index-update-ranks-2019>. In Scotland, deprivation data is for Data Zones, Intermediate Zones, and Council Areas based on data from <https://simd.scot>. In Northern Ireland, deprivation data is for Super Output Areas and Local Government Districts based on data from <https://www.nisra.gov.uk/statistics/deprivation/northern-ireland-multiple-deprivation-measure-2017-nimdm2017>. The IMD package also provides the composite UK index developed by <https://github.com/mysociety/composite_uk_imd>.

Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. ALE has a key advantage over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values represent a clean functional decomposition of the model. As such, ALE values are not affected by the presence or absence of interactions among variables in a mode. Moreover, its computation is relatively rapid. This package reimplements the algorithms for calculating ALE data and develops highly interpretable visualizations for plotting these ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference. For more details, see Okoli, Chitu. 2023. â Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).â arXiv. <doi:10.48550/arXiv.2310.09877>.

Nonparametric efficiency measurement by data envelopment analysis. Provides radial (Charnes-Cooper-Rhodes and Banker-Charnes-Cooper) technical efficiency under constant, variable, non-increasing and non-decreasing returns to scale, the slacks-based measure of Tone (2001), the additive model of Charnes and others (1985), and the directional distance function of Chambers, Chung and Fare (1996), all through one interface and one result object. Efficiency estimates are accompanied by peers, slacks, returns-to-scale classification, scale efficiency and the optimal multipliers, and by bias-corrected estimates and confidence intervals from the smoothed homogeneous bootstrap of Simar and Wilson (1998). Where prices are known, cost, revenue and Nerlovian profit efficiency separate the technical component from the allocative one; where they are not, cross-efficiency with the secondary goals of Doyle and Green (1994) ranks units that a self-appraisal leaves tied. This package succeeds the archived DEA package of Diaz-Martinez and Fernandez-Menendez (2008).

There is no ophthalmic researcher who has not had headaches from the handling of visual acuity entries. Different notations, untidy entries. This shall now be a matter of the past. Eye makes it as easy as pie to work with VA data - easy cleaning, easy conversion between Snellen, logMAR, ETDRS letters, and qualitative visual acuity shall never pester you again. The eye package automates the pesky task to count number of patients and eyes, and can help to clean data with easy re-coding for right and left eyes. It also contains functions to help reshaping eye side specific variables between wide and long format. Visual acuity conversion is based on Schulze-Bonsel et al. (2006) <doi:10.1167/iovs.05-0981>, Gregori et al. (2010) <doi:10.1097/iae.0b013e3181d87e04>, Beck et al. (2003) <doi:10.1016/s0002-9394(02)01825-1> and Bach (2007) <https://michaelbach.de/sci/acuity.html>.

This package provides statistical methods for auditing as implemented in JASP for Audit (Derks et al., 2021 <doi:10.21105/joss.02733>). First, the package makes it easy for an auditor to plan a statistical sample, select the sample from the population, and evaluate the misstatement in the sample compliant with international auditing standards. Second, the package provides statistical methods for auditing data, including tests of digit distributions and repeated values. Finally, the package includes methods for auditing algorithms on the aspect of fairness and bias. Next to classical statistical methodology, the package implements Bayesian equivalents of these methods whose statistical underpinnings are described in Derks et al. (2021) <doi:10.1111/ijau.12240>, Derks et al. (2024) <doi:10.2308/AJPT-2021-086>, Derks et al. (2026) <doi:10.1504/IJADS.2027.10078699> Derks et al. (2026) <doi:10.1111/ijau.70027>, and Derks et al. (2025) <doi:10.31234/osf.io/b8tu2>.

This package infers the extinction of a species from a time-ordered record of sightings, collecting methods from the sighting-record and stratigraphic-range literature under one interface. Includes endpoint estimators (Robson & Whitlock, 1964; Strauss & Sadler, 1989; Roberts & Solow, 2003), frequentist persistence tests (Solow, 1993a, 1993b; Burgman, Grimson & Ferson, 1995; Solow & Roberts, 2003; Solow, 2005; McInerny, Roberts, Davy & Cribb, 2006; JariÄ & Ebenhard, 2010), Bayesian models (Rout, Salomon & McCarthy, 2009; Caley & Barry, 2014), methods for sightings of uncertain reliability (Thompson et al., 2013; JariÄ & Roberts, 2014; Lee et al., 2014; Brook, Buettel & JariÄ , 2019; Kodikara et al., 2020, 2021), models combining records and surveys (Thompson et al., 2017, 2019), and confidence intervals on the end-points of fossil ranges (Marshall, 1994, 1997; Weiss & Marshall, 1999). Every method is tested against the published examples of its source paper and returns a common result class; persistence tests are inverted to give one-sided upper confidence bounds.

Univariate and multivariate SQC tools that completes and increases the SQC techniques available in R. Apart from integrating different R packages devoted to SQC ('qcc','MSQC'), provides nonparametric tools that are highly useful when Gaussian assumption is not met. This package computes standard univariate control charts for individual measurements, X-bar', S', R', p', np', c', u', EWMA and CUSUM'. In addition, it includes functions to perform multivariate control charts such as Hotelling T2', MEWMA and MCUSUM'. As representative feature, multivariate nonparametric alternatives based on data depth are implemented in this package: r', Q and S control charts. In addition, Phase I and II control charts for functional data are included. This package also allows the estimation of the most complete set of capability indices from first to fourth generation, covering the nonparametric alternatives, and performing the corresponding capability analysis graphical outputs, including the process capability plots. See Flores et al. (2021) <doi:10.32614/RJ-2021-034>.

The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group. Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time. This package contains tools for computing average treatment effect parameters in Difference in Differences setups with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>. The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a particular time. These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these. There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.

Introduction to some novel accurate hybrid methods of geostatistical and machine learning methods for spatial predictive modelling. It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method. For details please see: Li, J., Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010) <https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/71407> Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) <doi:10.1016/j.csr.2011.05.015> Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) <doi:10.1016/j.envsoft.2011.07.004> Li, J., Potter, A., Huang, Z. and Heap, A. (2012) <https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/74030>.

Rule-driven, auditable cleaning of survey and assessment response data, implementing the WeianData Detect-Execute-Report workflow. Provides a multi-format, multi-encoding input layer (CSV, Excel', SPSS', Stata', SAS', Parquet, JSON), the dcc_data container with a provenance chain, level-0 structural diagnostics, five built-in response-quality detectors (missing items, straight-lining, response time, trap items, score anomalies), a declarative YAML rule engine, an execution engine with a cell-level audit log, answer-key scoring, multi-form to master item bank mapping, a normalized report model rendered as bilingual staff workbooks and HTML, complete statistical bundles, and versioned machine JSON/JSONL with findings-to-changes reconciliation, cell-level lineage tracing, and manifest-based one-command reproduction. Includes a protected bilingual strict project workbook and matching JSON contract with cell-addressed validation, non-mutating preflight, preview-first execution, and localized staff guidance. All formally supported input backends install with the package; PDF is optional rather than a fixed report output.

This package provides methods and tools for (pre-)processing of metabolomics datasets (i.e. peak matrices), including filtering, normalisation, missing value imputation, scaling, and signal drift and batch effect correction methods. Filtering methods are based on: the fraction of missing values (across samples or features); Relative Standard Deviation (RSD) calculated from the Quality Control (QC) samples; the blank samples. Normalisation methods include Probabilistic Quotient Normalisation (PQN) and normalisation to total signal intensity. A unified user interface for several commonly used missing value imputation algorithms is also provided. Supported methods are: k-nearest neighbours (knn), random forests (rf), Bayesian PCA missing value estimator (bpca), mean or median value of the given feature and a constant small value. The generalised logarithm (glog) transformation algorithm is available to stabilise the variance across low and high intensity mass spectral features. Finally, this package provides an implementation of the Quality Control-Robust Spline Correction (QCRSC) algorithm for signal drift and batch effect correction of mass spectrometry-based datasets.

Incorporating node-level covariates for community detection has gained increasing attention these years. This package provides the function for implementing the novel community detection algorithm known as Network-Adjusted Covariates for Community Detection (NAC), which is designed to detect latent community structure in graphs with node-level information, i.e., covariates. This algorithm can handle models such as the degree-corrected stochastic block model (DCSBM) with covariates. NAC specifically addresses the discrepancy between the community structure inferred from the adjacency information and the community structure inferred from the covariates information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2023) <arXiv:2306.15616>. In addition to NAC, this package includes several other existing community detection algorithms that are compared to NAC in the reference paper. These algorithms are Spectral Clustering On Ratios-of Eigenvectors (SCORE), network-based regularized spectral clustering (Net-based), covariate-based spectral clustering (Cov-based), covariate-assisted spectral clustering (CAclustering) and semidefinite programming (SDP).

Mortality rates are typically provided in an abridged format, i.e., by age groups 0, [1, 5], [5, 10]', [10, 15]', and so on. Some applications necessitate a detailed (single) age description. Despite the large number of proposed approaches in the literature, only a few methods ensure great performance at both younger and higher ages. For example, the 6-term Lagrange interpolation function is well suited to mortality interpolation at younger ages (with irregular intervals), but not at older ages. The Karup-King method, on the other hand, performs well at older ages but is not suitable for younger ones. Interested readers can find a full discussion of the two stated methods in the book Shryock, Siegel, and Associates (1993).The Q2q package combines the two methods to allow for the interpolation of mortality rates across all age groups. It begins by implementing each method independently, and then the resulting curves are linked using a 5-age averaged error between the two partial curves.

Fetch Australian Taxation Office (ATO) Taxation Statistics and related datasets via the data.gov.au Comprehensive Knowledge Archive Network ('CKAN') API <https://data.gov.au/data/api/3/>. Provides tidy access to individual, company, superannuation, goods and services tax (GST), fringe benefits tax (FBT), Voluntary Tax Transparency Code (VTTC), Pay As You Go (PAYG) withholding, charity, excise, and Corporate Tax Transparency data, plus Petroleum Resource Rent Tax, Medicare Levy Surcharge, and fuel tax credit aggregates. Includes reproducibility helpers (snapshot pinning, SHA-256 cache integrity, session manifest, optional Zenodo deposit), classification crosswalks (ANZSIC 2006 to 2020, ANZSCO 2013 to 2021), panel harmonisation, reconciliation against Final Budget Outcome totals, and real-terms and per-capita helpers backed by bundled Australian Bureau of Statistics (ABS) Consumer Price Index and Estimated Resident Population series. Bridges to the taxstats 2 per cent microdata sample via column-schema mapping. Data is published by the Australian Taxation Office under Creative Commons Attribution 2.5 Australia or 3.0 Australia licences (dataset-dependent).

The function plotLRT() draws pairwise graphical model checks for the Rasch Model (RM; Rasch, 1960), the Partial Credit Model (PCM; Masters, 1982), and the Rating Scale Model (RSM; Andrich, 1978) using the output object of eRm::LRtest(). The function cLRT() provides a conditional Likelihood Ratio Test (Andersen, 1973), using the routines of psychotools (Zeileis et al., 2026). Users may choose to plot the threshold parameters, the cumulative thresholds, the average thresholds per item, or the person parameters. Extended coloring options allow for automated item-wise or threshold-wise coloring. For multi-group splits, all pairwise group comparisons are drawn automatically. The function plotMLT() draws a graphical model check for the Martin-Loef-Test (MLT). If type='persons', it draws scatterplots of the person parameter estimates of each pair of item sub-groups. If type='items', it draws a scatterplot of the item/threshold parameter estimates of each of the two sub-groups vertically against the estimates of the total sample horizontally.

The kernelSmoothing() function allows you to square and smooth geolocated data. It calculates a classical kernel smoothing (conservative) or a geographically weighted median. There are four major call modes of the function. The first call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth) for a classical kernel smoothing and automatic grid. The second call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles) for a geographically weighted median and automatic grid. The third call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, centroids) for a classical kernel smoothing and user grid. The fourth call mode is kernelSmoothing(obs, epsg, cellsize, bandwidth, quantiles, centroids) for a geographically weighted median and user grid. Geographically weighted summary statistics : a framework for localised exploratory data analysis, C.Brunsdon & al., in Computers, Environment and Urban Systems C.Brunsdon & al. (2002) <doi:10.1016/S0198-9715(01)00009-6>, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition, Diggle, pp. 83-86, (2003) <doi:10.1080/13658816.2014.937718>.

This package provides a toolkit for creating, importing, validating, enriching, rendering, and depositing Key Resources Tables (KRTs). A KRT lists the resources used and generated in a study (antibodies, cell lines, organisms, chemicals, software, datasets, protocols, and more), each paired with a persistent identifier such as a Research Resource Identifier (RRID), a Digital Object Identifier (DOI), a repository accession, or a catalog number, so that resources are unambiguously identifiable and machine-actionable. The package models resources as typed, validated records around a neutral core schema and maps them to journal or funder output profiles, following the FAIR (Findable, Accessible, Interoperable, Reusable) principles of Wilkinson et al. (2016) <doi:10.1038/sdata.2016.18>. It normalizes and optionally resolves identifiers against public registries, extracts resources from manuscripts, and renders tables both in the STAR (Structured, Transparent, Accessible Reporting) Methods style used by Cell Press journals and in the style required by ASAP (Aligning Science Across Parkinson's), with an emphasis on transparency, reproducibility, and correct per-component licensing.

Implementations of the kernel measure of multi-sample dissimilarity (KMD) between several samples using K-nearest neighbor graphs and minimum spanning trees. The KMD measures the dissimilarity between multiple samples, based on the observations from them. It converges to the population quantity (depending on the kernel) which is between 0 and 1. A small value indicates the multiple samples are from the same distribution, and a large value indicates the corresponding distributions are different. The population quantity is 0 if and only if all distributions are the same, and 1 if and only if all distributions are mutually singular. The package also implements the tests based on KMD for H0: the M distributions are equal against H1: not all the distributions are equal. Both permutation test and asymptotic test are available. These tests are consistent against all alternatives where at least two samples have different distributions. For more details on KMD and the associated tests, see Huang, Z. and B. Sen (2024) <doi:10.1080/01621459.2023.2298036>.

Developed for computing the probability density function, computing the cumulative distribution function, computing the quantile function, random generation, drawing q-q plot, and estimating the parameters of 24 G-family of statistical distributions via the maximum product spacing approach introduced in <https://www.jstor.org/stable/2345411>. The set of families contains: beta G distribution, beta exponential G distribution, beta extended G distribution, exponentiated G distribution, exponentiated exponential Poisson G distribution, exponentiated generalized G distribution, exponentiated Kumaraswamy G distribution, gamma type I G distribution, gamma type II G distribution, gamma uniform G distribution, gamma-X generated of log-logistic family of G distribution, gamma-X family of modified beta exponential G distribution, geometric exponential Poisson G distribution, generalized beta G distribution, generalized transmuted G distribution, Kumaraswamy G distribution, log gamma type I G distribution, log gamma type II G distribution, Marshall Olkin G distribution, Marshall Olkin Kumaraswamy G distribution, modified beta G distribution, odd log-logistic G distribution, truncated-exponential skew-symmetric G distribution, and Weibull G distribution.