Aims to support all features of the system credential store, including non-portable ones. Supports Keychain on macOS', and Credential Manager on Windows'. See the keyring package if you need a portable API'.

This package provides functions for creating ensembles of optimal trees for regression, classification (Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). (2019) <doi:10.1007/s11634-019-00364-9>) and class membership probability estimation (Khan, Z, Gul, A, Mahmoud, O, Miftahuddin, M, Perperoglou, A, Adler, W & Lausen, B (2016) <doi:10.1007/978-3-319-25226-1_34>) are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Three different methods of tree selection for the case of classification are given. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data.

An assortment of helper functions for managing data (e.g., rotating values in matrices by a user-defined angle, switching from row- to column-indexing), dates (e.g., intuiting year from messy date strings), handling missing values (e.g., removing elements/rows across multiple vectors or matrices if any have an NA), text (e.g., flushing reports to the console in real-time); and combining data frames with different schema (copying, filling, or concatenating columns or applying functions before combining).

Data used in compiling the Handbook of UK Urban Tree Allometric Equations and Size Characteristics (Fennel 2024). The data include measurements of height, crown radius and diameter at breast height (DBH) for UK urban trees. For more details see Fennell (2024) Handbook of UK Urban Tree Allometric Equations and Size Characteristics (Version 1.4). <doi:10.13140/RG.2.2.28745.04961>.

Fits two-dimensional data by means of orthogonal nonlinear least-squares using Levenberg-Marquardt minimization and provides functionality for fit diagnostics and plotting. Delivers the same results as the ODRPACK Fortran implementation described in Boggs et al. (1989) <doi:10.1145/76909.76913>, but is implemented in pure R.

Many treatment effect estimators can be written as weighted outcomes. These weights have established use cases like checking covariate balancing via packages like cobalt'. This package takes the original estimator objects and outputs these outcome weights. It builds on the general framework of Knaus (2024) <doi:10.48550/arXiv.2411.11559>. This version is compatible with the grf package and provides an internal implementation of Double Machine Learning.

Perform a Bayesian estimation of the ordinal exploratory Higher-order General Diagnostic Model (OHOEGDM) for Polytomous Data described by Culpepper, S. A. and Balamuta, J. J. (2021) <doi:10.1080/00273171.2021.1985949>.

This package implements the orthogonal reparameterization approach recommended by Lancaster (2002) to estimate dynamic panel models with fixed effects (and optionally: panel specific intercepts). The approach uses a likelihood-based estimator and produces estimates that are asymptotically unbiased as N goes to infinity, with a T as low as 2.

Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). The package includes functions to perform independent component analysis - orthogonal signal deconvolution (ICA-OSD), independent component regression (ICR), multivariate curve resolution (MCR-ALS) and orthogonal signal deconvolution (OSD) alone.

The popular population genetic software Treemix by Pickrell and Pritchard (2012) <DOI:10.1371/journal.pgen.1002967> estimates the number of migration edges on a population tree. However, it can be difficult to determine the number of migration edges to include. Previously, it was customary to stop adding migration edges when 99.8% of variation in the data was explained, but OptM automates this process using an ad hoc statistic based on the second-order rate of change in the log likelihood. OptM also has added functionality for various threshold modeling to compare with the ad hoc statistic.

This package provides new tools for analyzing discrete trait data integrating bio-ontologies and phylogenetics. It expands on the previous work of Tarasov et al. (2019) <doi:10.1093/isd/ixz009>. The PARAMO pipeline allows to reconstruct ancestral phenomes treating groups of morphological traits as a single complex character. The pipeline incorporates knowledge from ontologies during the amalgamation of individual character stochastic maps. Here we expand the current PARAMO functionality by adding new statistical methods for inferring evolutionary phenome dynamics using non-homogeneous Poisson process (NHPP). The new functionalities include: (1) reconstruction of evolutionary rate shifts of phenomes across lineages and time; (2) reconstruction of morphospace dynamics through time; and (3) estimation of rates of phenome evolution at different levels of anatomical hierarchy (e.g., entire body or specific regions only). The package also includes user-friendly tools for visualizing evolutionary rates of different anatomical regions using vector images of the organisms of interest.

Estimates out-of-sample R² through bootstrap or cross-validation as a measure of predictive performance. In addition, a standard error for this point estimate is provided, and confidence intervals are constructed.

Providing mean partition for ensemble clustering by optimal transport alignment(OTA), uncertainty measures for both partition-wise and cluster-wise assessment and multiple visualization functions to show uncertainty, for instance, membership heat map and plot of covering point set. A partition refers to an overall clustering result. Jia Li, Beomseok Seo, and Lin Lin (2019) <doi:10.1002/sam.11418>. Lixiang Zhang, Lin Lin, and Jia Li (2020) <doi:10.1093/bioinformatics/btaa165>.

Apache OpenNLP jars and basic English language models.

This package provides a database resource that is accessible through the Open Database Connectivity ('ODBC') API. This package uses the Resource model, with URL "resolver" and "client", to dynamically discover and make accessible tables stored in a MS SQL Server database. For more details see Marcon (2021) <doi:10.1371/journal.pcbi.1008880>.

Classify Open Street Map (OSM) features into meaningful functional or analytical categories. Designed for OSM PBF files, e.g. from <https://download.geofabrik.de/> imported as spatial data frames. A classification consists of a list of categories that are related to certain OSM tags and values. Given a layer from an OSM PBF file and a classification, the main osm_classify() function returns a classification data table giving, for each feature, the primary and alternative categories (if there is overlap) assigned, and the tag(s) and value(s) matched on. The package also contains a classification of OSM features by economic function/significance, following Krantz (2023) <https://www.ssrn.com/abstract=4537867>.

Ordinal data classification, clustering and co-clustering using model-based approach with the BOS (Binary Ordinal Search) distribution for ordinal data (Christophe Biernacki and Julien Jacques (2016) <doi:10.1007/s11222-015-9585-2>).

Calculate ocean wave height summary statistics and process data from bottom-mounted pressure sensor data loggers. Derived primarily from MATLAB functions provided by U. Neumeier at <http://neumeier.perso.ch/matlab/waves.html>. Wave number calculation based on the algorithm in Hunt, J. N. (1979, ISSN:0148-9895) "Direct Solution of Wave Dispersion Equation", American Society of Civil Engineers Journal of the Waterway, Port, Coastal, and Ocean Division, Vol 105, pp 457-459.

This package provides a DBI-compatible interface to ODBC databases.

Identifies an optimal transformation of a surrogate marker such that the proportion of treatment effect explained can be inferred based on the transformation of the surrogate and nonparametrically estimates two model-free quantities of this proportion. Details are described in Wang et al (2020) <doi:10.1093/biomet/asz065>.

This package creates block designs of fixed size J with at least one treated and control unit per block. Blocks larger than pairs better distinguish effects caused by a treatment from unmeasured confounding in assignment of individuals to treatment. Somewhat counterintuitively, blocks larger than pairs can use more units while attaining better covariate balance and block homogeneity. A forthcoming manuscript by Brumberg and Rosenbaum details the design.

Machine learning estimator specifically optimized for predictive modeling of ordered non-numeric outcomes. ocf provides forest-based estimation of the conditional choice probabilities and the covariatesâ marginal effects. Under an "honesty" condition, the estimates are consistent and asymptotically normal and standard errors can be obtained by leveraging the weight-based representation of the random forest predictions. Please reference the use as Di Francesco (2025) <doi:10.1080/07474938.2024.2429596>.

The log-rank test is performed to assess the survival outcomes between two group. When there is no proper control group or obtaining such data is cumbersome, one sample log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003)<doi:10.1093/jnci/djt227> and variation of the test for small sample sizes which is detailed in FD Liddell (1984)<doi:10.1136/jech.38.1.85> paper. Visualization function in the package generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest.

Seamlessly build and manipulate graph structures, leveraging its high-performance methods for filtering, joining, and mutating data. Ensures that mutations and changes to the graph are performed in place, streamlining your workflow for optimal productivity.