This package provides methods for fitting bivariate lines in allometry using the major axis (MA) or standardised major axis (SMA), and for making inferences about such lines. The available methods of inference include confidence intervals and one-sample tests for slope and elevation, testing for a common slope or elevation amongst several allometric lines, constructing a confidence interval for a common slope or elevation, and testing for no shift along a common axis, amongst several samples. See Warton et al. 2012 <doi:10.1111/j.2041-210X.2011.00153.x> for methods description.

Calculate geolocations by light using template matching. The routine uses a calibration free optimization of a sky illuminance model to determine locations robustly using a template matching approach, as described by Ekstrom (2004) <https://nipr.repo.nii.ac.jp/records/2496>, and behaviourly informed constraints (step-selection).

Data sets and functions to support the books "Statistics: Data analysis and modelling" by Speekenbrink, M. (2021) <https://mspeekenbrink.github.io/sdam-book/> and "An R companion to Statistics: data analysis and modelling" by Speekenbrink, M. (2021) <https://mspeekenbrink.github.io/sdam-r-companion/>. All datasets analysed in these books are provided in this package. In addition, the package provides functions to compute sample statistics (variance, standard deviation, mode), create raincloud and enhanced Q-Q plots, and expand Anova results into omnibus tests and tests of individual contrasts.

This package provides functions and benchmark datasets for the economic appraisal of soil and water conservation (SWC) measures in watershed development projects. Implements benefit-cost ratio (BCR), net present value (NPV), internal rate of return (IRR) via the bisection method of Brent (1973, ISBN:9780130223715), modified BCR, marginal rate of return using the CIMMYT (1988, ISBN:9686127127) method, payback period, soil loss economic valuation via the Universal Soil Loss Equation of Wischmeier and Smith (1978, ISBN:0160016258), groundwater recharge valuation, employment generation ratio, sensitivity analysis, switching value analysis, and Monte Carlo simulation. Six datasets are included: state-wise BCR benchmarks from NABARD (2019) watershed evaluations, USLE erodibility parameters for Indian soil orders from NBSS and LUP, rainfall erosivity for twenty Indian districts from IMD data, SWC unit cost norms from PMKSY-WDC (GoI 2015), and two hypothetical datasets for illustration. Methods follow Gittinger (1982, ISBN:9780801825439) and Squire and van der Tak (1975, ISBN:9780801816697).

This package implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models).

The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.

Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.

Selection index is one of the efficient and acurrate method for selection of animals. This package is useful for construction of selection indices. It uses mixed and random model least squares analysis to estimate the heritability of traits and genetic correlation between traits. The package uses the sire model as it is considered as random effect. The genetic and phenotypic (co)variances along with the relative economic values are used to construct the selection index for any number of traits. It also estimates the accuracy of the index and the genetic gain expected for different traits. Fisher (1936) <doi:10.1111/j.1469-1809.1936.tb02137.x>.

This package provides crop yield and meteorological data for Ontario, Canada. Includes functions for fitting and predicting data using spatio-temporal models, as well as tools for visualizing the results. The package builds upon existing R packages, including copula (Hofert et al., 2025) <doi:10.32614/CRAN.package.copula>, and bsts (Scott, 2024) <doi:10.32614/CRAN.package.bsts>.

This package provides datasets from Vigen (2015) <https://web.archive.org/web/20230607181247/https%3A/tylervigen.com/spurious-correlations> rescued from the Internet Wayback Machine. These should be preserved for statistics introductory courses as these make it very clear that correlation is not causation.

This package provides methods for the analysis of signed networks. This includes several measures for structural balance as introduced by Cartwright and Harary (1956) <doi:10.1037/h0046049>, blockmodeling algorithms from Doreian (2008) <doi:10.1016/j.socnet.2008.03.005>, various centrality indices, and projections of signed two-mode networks introduced by Schoch (2020) <doi:10.1080/0022250X.2019.1711376>.

The goal of snpsettest is to provide simple tools that perform set-based association tests (e.g., gene-based association tests) using GWAS (genome-wide association study) summary statistics. A set-based association test in this package is based on the statistical model described in VEGAS (versatile gene-based association study), which combines the effects of a set of SNPs accounting for linkage disequilibrium between markers. This package uses a different approach from the original VEGAS implementation to compute set-level p values more efficiently, as described in <https://github.com/HimesGroup/snpsettest/wiki/Statistical-test-in-snpsettest>.

This package provides a collection of tools for analyzing significance of assets, funds, and trading strategies, based on the Sharpe ratio and overfit of the same. Provides density, distribution, quantile and random generation of the Sharpe ratio distribution based on normal returns, as well as the optimal Sharpe ratio over multiple assets. Computes confidence intervals on the Sharpe and provides a test of equality of Sharpe ratios based on the Delta method. The statistical foundations of the Sharpe can be found in the author's Short Sharpe Course <doi:10.2139/ssrn.3036276>.

R client and utilities for Seven Bridges Platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms. API documentation is hosted publicly at <https://docs.sevenbridges.com/docs/the-api>.

Integrates the 13C nuclear magnetic resonance spectra using different integration ranges. Output depends on the method chosen. For the Molecular Mixing Model, a measurement of the fitting quality is given by its R-factor. For more details see: <doi:10.5281/zenodo.10137768>.

Operationalizes the identification problem of which subset of items should be kept in the shortened version of a said psychometric instrument to best represent the set of items comprised in the original version of the said psychometric instrument.

An implementation of a single-index regression for optimizing individualized dose rules from an observational study. To model interaction effects between baseline covariates and a treatment variable defined on a continuum, we employ two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear combination of the covariates (a single-index). An unspecified main effect for the covariates is allowed, which can also be modeled through a parametric model. A unique contribution of this work is in the parsimonious single-index parametrization specifically defined for the interaction effect term. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (for the case of a discrete treatment) and Park, Petkova, Tarpey, and Ogden (2021) "A single-index model with a surface-link for optimizing individualized dose rules" <arXiv:2006.00267v2> for detail of the method. The model can take a member of the exponential family as a response variable and can also take an ordinal categorical response. The main function of this package is simsl().

Estimates a covariance matrix using Stein's isotonized covariance estimator, or a related estimator suggested by Haff.

This package implements a test for distinguishing between true long memory and spurious long memory. Reference: Qu, Z. (2011). "A Test Against Spurious Long Memory." Journal of Business & Economic Statistics, 29(3), 423â 438. <doi:10.1198/jbes.2010.09153>.

Vignettes for the survival package. Split from the survival package since the vignettes were getting large. Also, since survival is a recommended package it cannot make use of other packages outside of base+recommended (e.g. rmarkdown').

This is a core implementation of SAS procedures for linear models - GLM, REG, ANOVA, TTEST, FREQ, and UNIVARIATE. Some R packages provide Type II and Type III SS. However, the results of nested and complex designs are often different from those of SAS'. Different results do not necessarily mean incorrectness. However, many want the same results as SAS'. This package aims to achieve that. Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).

This package provides a generic framework for working with YAML (YAML Ain't Markup Language) configuration files. Uses ajv (Another JSON Schema Validator) via V8 to validate configurations against JSON Schema definitions. Configuration objects inherit from S7 classes and base lists, supporting downstream extension through custom classes and methods.

The Brazilian system for diploma registration and validation on technical and superior courses are managing by Sistec platform, see <https://sistec.mec.gov.br/>. This package provides tools for Brazilian institutions to update the student's registration and make data analysis about their situation, retention and drop out.

It's a collection of functions for Multiplicity Correction and Multiple Testing.