Linear model calculations are made for many random versions of data. Using residual randomization in a permutation procedure, sums of squares are calculated over many permutations to generate empirical probability distributions for evaluating model effects. Additionally, coefficients, statistics, fitted values, and residuals generated over many permutations can be used for various procedures including pairwise tests, prediction, classification, and model comparison. This package should provide most tools one could need for the analysis of high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.

Carry out principal component analysis (PCA) of very large pedigrees such as found in breeding populations! This package exploits sparse matrices and randomised linear algebra to deliver a gazillion-times speed-up compared to naive singular value decoposition (SVD) (and eigen decomposition).

Search by keywords in R packages, task views, CRAN, the web and display the results in the console or in txt, html or pdf files. Download the package documentation (html index, README, NEWS, pdf manual, vignettes, source code, binaries) with a single instruction. Visualize the package dependencies and CRAN checks. Compare the package versions, unload and install the packages and their dependencies in a safe order. Explore CRAN archives. Use the above functions for task view maintenance. Access web search engines from the console thanks to 80+ bookmarks. All functions accept standard and non-standard evaluation.

Read Statistical Data and Metadata Exchange (SDMX) XML data. This the main transmission format used in official statistics. Data can be imported from local SDMX-ML files or a SDMX web-service and will be read in as is into a dataframe object. The RapidXML C++ library <https://rapidxml.sourceforge.net/> is used to parse the XML data.

Translation of the MATLAB program Carb (Nathan and Mauz 2008 <DOI:10.1016/j.radmeas.2007.12.012>; Mauz and Hoffmann 2014) for dose rate modelling for carbonate-rich samples in the context of trapped charged dating (e.g., luminescence dating) applications.

This package provides an interface to the Python package Geomstats authored by Miolane et al. (2020) <arXiv:2004.04667>.

This package provides functions to safely map from a vector of keys to a vector of values, determine properties of a given relation, or ensure a relation conforms to a given type, such as many-to-many, one-to-many, injective, surjective, or bijective. Permits default return values for use similar to a vectorised switch statement, as well as safely handling large vectors, NAs, and duplicate mappings.

It computes the Schmidt decomposition of bipartite quantum systems, discrete or continuous, and their respective entanglement metrics. See Artur Ekert, Peter L. Knight (1995) <doi:10.1119/1.17904> for more details.

Implementations of several robust nonparametric two-sample tests for location or scale differences. The test statistics are based on robust location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators as described in Fried & Dehling (2011) <doi:10.1007/s10260-011-0164-1>. The p-values can be computed via the permutation principle, the randomization principle, or by using the asymptotic distributions of the test statistics under the null hypothesis, which ensures (approximate) distribution independence of the test decision. To test for a difference in scale, we apply the tests for location difference to transformed observations; see Fried (2012) <doi:10.1016/j.csda.2011.02.012>. Random noise on a small range can be added to the original observations in order to hold the significance level on data from discrete distributions. The location tests assume homoscedasticity and the scale tests require the location parameters to be zero.

Downloads, imports, and tidies time series data from the Australian Bureau of Statistics <https://www.abs.gov.au/>.

Exports an Rcpp interface for the Bessel functions in the Bessel package, which can then be called from the C++ code of other packages. For the original Fortran implementation of these functions see Amos (1995) <doi:10.1145/212066.212078>.

An R package for estimating conditional multivariate reference regions. The reference region is non parametrically estimated using a kernel density estimator. Covariates effects on the multivariate response means vector and variance-covariance matrix, thus on the region shape, are estimated by flexible additive predictors. Continuous covariates non linear effects might be estimated using penalized splines smoothers. Confidence intervals for the covariates estimated effects might be derived from bootstrap resampling. Kernel density bandwidth can be estimated with different methods, including a method that optimize the region coverage. Numerical, and graphical, summaries can be obtained by the user in order to evaluate reference region performance with real data. Full mathematical details can be found in <doi:10.1002/sim.9163> and <doi:10.1007/s00477-020-01901-1>.

We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998617749561>.

This package provides a platform-independent GUI for design of experiments. The package is implemented as a plugin to the R-Commander, which is a more general graphical user interface for statistics in R based on tcl/tk. DoE functionality can be accessed through the menu Design that is added to the R-Commander menus.

This package provides a toolbox created by members of the International Union for Conservation of Nature (IUCN) Red List of Ecosystems Committee for Scientific Standards. Primarily, it is a set of tools suitable for calculating the metrics required for making assessments of species and ecosystems against the IUCN Red List of Threatened Species and the IUCN Red List of Ecosystems categories and criteria. See the IUCN website for detailed guidelines, the criteria, publications and other information.

This package contains functions useful for reading in Licor 6800 files, correcting and analyzing rapid A/Ci response (RACiR) data. Requires some user interaction to adjust the calibration (empty chamber) data file to a useable range. Calibration uses a 1st to 5th order polynomial as suggested in Stinziano et al. (2017) <doi:10.1111/pce.12911>. Data can be processed individually or batch processed for all files paired with a given calibration file. RACiR is a trademark of LI-COR Biosciences, and used with permission.

Analysis of corneal data obtained from a Placido disk corneal topographer with calculation of irregularity indices. This package performs analyses of corneal data obtained from a Placido disk corneal topographer, with the calculation of the Placido irregularity indices and the posterior analysis. The package is intended to be easy to use by a practitioner, providing a simple interface and yielding easily interpretable results. A corneal topographer is an ophthalmic clinical device that obtains measurements in the cornea (the anterior part of the eye). A Placido disk corneal topographer makes use of the Placido disk [Rowsey et al. (1981)]<doi:10.1001/archopht.1981.03930011093022>, which produce a circular pattern of measurement nodes. The raw information measured by such a topographer is used by practitioners to analyze curvatures, to study optical aberrations, or to diagnose specific conditions of the eye (e.g. keratoconus, an important corneal disease). The rPACI package allows the calculation of the corneal irregularity indices described in [Castro-Luna et al. (2020)]<doi:10.1016%2Fj.clae.2019.12.006>, [Ramos-Lopez et al. (2013)]<doi:10.1097%2FOPX.0b013e3182843f2a>, and [Ramos-Lopez et al. (2011)]<doi:10.1097/opx.0b013e3182279ff8>. It provides a simple interface to read corneal topography data files as exported by a typical Placido disk topographer, to compute the irregularity indices mentioned before, and to display summary plots that are easy to interpret for a clinician.

This package provides a tool designed to analyze recurrent events when dealing with right-censored data and the potential presence of a terminal event (that prevents further occurrences, like death). It extends the random survival forest algorithm, adapting splitting rules and node estimators to handle complexities of recurrent events. The methodology is fully described in Murris, J., Bouaziz, O., Jakubczak, M., Katsahian, S., & Lavenu, A. (2024) (<https://hal.science/hal-04612431v1/document>).

Estimates the rank intraclass correlation coefficient (ICC) for clustered continuous and ordinal data. See Tu et al. (2023) <DOI:10.1002/sim.9864> for details.

Parse scientific names using gnparser (<https://github.com/gnames/gnparser>), written in Go. gnparser parses scientific names into their component parts; it utilizes a Parsing Expression Grammar specifically for scientific names.

This package provides a set of tools to process and calculate metrics on point clouds derived from terrestrial LiDAR (Light Detection and Ranging; TLS). Its creation is based on key aspects of the TLS application in forestry and ecology. Currently, the main routines are based on filtering, neighboring features of points, voxelization, canopy structure, and the creation of artificial stands. It is written using data.table and C++ language and in most of the functions it is possible to use parallel processing to speed-up the routines.

Helps to fit thermal performance curves (TPCs). rTPC contains 26 model formulations previously used to fit TPCs and has helper functions to set sensible start parameters, upper and lower parameter limits and estimate parameters useful in downstream analyses, such as cardinal temperatures, maximum rate and optimum temperature. See Padfield et al. (2021) <doi:10.1111/2041-210X.13585>.

This package provides a convenient way of accessing data published by the Reserve Bank of New Zealand (RBNZ) on their website, <https://www.rbnz.govt.nz/statistics>. A range of financial and economic data is provided in spreadsheet format including exchange and interest rates, commercial lending statistics, Reserve Bank market operations, financial institution statistics, household financial data, New Zealand debt security information, and economic indicators. This package provides a method to download those spreadsheets and read them directly into R.

Computes a variety of statistics for relational event models. Relational event models enable researchers to investigate both exogenous and endogenous factors influencing the evolution of a time-ordered sequence of events. These models are categorized into tie-oriented models (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>), where the probability of a dyad interacting next is modeled in a single step, and actor-oriented models (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>), which first model the probability of a sender initiating an interaction and subsequently the probability of the sender's choice of receiver. The package is designed to compute a variety of statistics that summarize exogenous and endogenous influences on the event stream for both types of models.