This package provides Python'-style list comprehensions. List comprehension expressions use usual loops (for(), while() and repeat()) and usual if() as list producers. In many cases it gives more concise notation than standard "*apply + filter" strategy.

Create correlation (or partial correlation) matrices. Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with corx'.

This package provides a collection of tools for designing, implementing, testing, documenting and visualizing dynamic simulation cropping system models. Models are specified as a combination of state variables, parameters, intermediate factors and input data that define a system of ordinary differential equations. Specified models can be used to simulate dynamic processes using numerical integration algorithms.

Estimates group-, subgroup-, and individual-level dynamic structures from multivariate intensive longitudinal data using continuous-time state-space models. The subgrouping procedure combines iterative shared-path searches with recurrent-evidence feature screening and partitioning around medoids. The continuous-time group iterative multiple model estimation method is described in Park et al. (2025) <doi:10.1080/10705511.2024.2429544>.

Access chemical, hazard, bioactivity, and exposure data from the Computational Toxicology and Exposure ('CTX') APIs <https://www.epa.gov/comptox-tools/computational-toxicology-and-exposure-apis>. ctxR was developed to streamline the process of accessing the information available through the CTX APIs without requiring prior knowledge of how to use APIs. Most data is also available on the CompTox Chemical Dashboard ('CCD') <https://comptox.epa.gov/dashboard/> and other resources found at the EPA Computational Toxicology and Exposure Online Resources <https://www.epa.gov/comptox-tools>.

Fits convolution-based nonstationary Gaussian process models to point-referenced spatial data. The nonstationary covariance function allows the user to specify the underlying correlation structure and which spatial dependence parameters should be allowed to vary over space: the anisotropy, nugget variance, and process variance. The parameters are estimated via maximum likelihood, using a local likelihood approach. Also provided are functions to fit stationary spatial models for comparison, calculate the Kriging predictor and standard errors, and create various plots to visualize nonstationarity.

Includes climate data from Japan Meteorological Agency ('JMA') <https://www.jma.go.jp/jma/indexe.html>. Can download climate data from JMA'.

Comprehensive suite of Granger causality tests for time series and panel data. For time series: Toda-Yamamoto (1995) <doi:10.1016/0304-4076(94)01616-8>, Fourier-based tests with single frequency (Enders and Jones, 2016) <doi:10.1515/snde-2014-0101> and cumulative frequencies (Nazlioglu et al., 2019) <doi:10.1080/1540496X.2018.1434072>, quantile causality tests (Cai et al., 2023) <doi:10.1016/j.frl.2023.104327>, and Bootstrap Fourier Granger Causality in Quantiles (Cheng et al., 2021) <doi:10.1007/s12076-020-00263-0>. For panel data: Panel Fourier Toda-Yamamoto (Yilanci and Gorus, 2020) <doi:10.1007/s11356-020-10092-9> and Panel Quantile Causality tests (Wang and Nguyen, 2022) <doi:10.1080/1331677X.2021.1952089>, as well as Group-Mean and Pooled Fully Modified OLS estimators for panel cointegrating polynomial regressions (Wagner and Reichold, 2023) <doi:10.1080/07474938.2023.2178141>. All tests include bootstrap inference for robust p-values.

This package provides tools to measure connection and independence between variables without relying on linear models. Includes functions to compute Eta squared, Chi-squared, and Cramer V. The main advantage of this package is that it works without requiring parametric assumptions. The methods implemented are based on educational material and statistical decomposition techniques, not directly on previously published software or articles.

The Copernicus Data Space Ecosystem, is an open ecosystem that provides free instant access to a wide range of data and services from the Copernicus Sentinel missions and more on our planetâ s land, oceans and atmosphere. This package provides entry points to several APIs allowing users to access the data directly in R.

We propose to determine the correction of the significance level after multiple coding of an explanatory variable in Generalized Linear Model. The different methods of correction of the p-value are the Single step Bonferroni procedure, and resampling based methods developed by P.H.Westfall in 1993. Resampling methods are based on the permutation and the parametric bootstrap procedure. If some continuous, and dichotomous transformations are performed this package offers an exact correction of the p-value developed by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available.

This package provides a collection of data sets for teaching cluster analysis.

Software to facilitates taking movement data in xyt format and pairing it with raster covariates within a continuous time Markov chain (CTMC) framework. As described in Hanks et al. (2015) <DOI:10.1214/14-AOAS803> , this allows flexible modeling of movement in response to covariates (or covariate gradients) with model fitting possible within a Poisson GLM framework.

Computes conditional multivariate normal densities, probabilities, and random deviates.

Wraps the Python codecarbon package via reticulate to measure the energy consumption and estimated carbon emissions of R code. Provides a self-contained setup routine that installs codecarbon into a dedicated conda environment, and an R-facing tracker API for measuring a block of code or a longer-running session.

This package provides infrastructure for interoperable ALTREP character vectors. Producers of ALTREP string classes can register access methods, allowing consumers to read supported character vectors through a common interface without materializing them as ordinary R strings. Also provides charvec', a reference ALTREP string implementation backed by stable memory slices, with support for efficient and multithreaded construction.

Design functions for DCMs and other types of choice studies (including MaxDiff and other tradeoffs).

Extends ACER ConQuest through a family of functions designed to improve graphical outputs and help with advanced analysis (e.g., differential item functioning). Allows R users to call ACER ConQuest from within R and read ACER ConQuest System Files (generated by the command `put` <https://conquestmanual.acer.org/s4-00.html#put>). Requires ACER ConQuest version 5.40 or later. A demonstration version can be downloaded from <https://shop.acer.org/acer-conquest-5.html>.

Generation of different Christmas cards, most of them being animated. Most of the cards can be generated in three languages (English, Catalan and Spanish). The collection started in 2009.

Design, workflow and statistical analysis of Cluster Randomised Trials of (health) interventions where there may be spillover between the arms (see <https://thomasasmith.github.io/index.html>).

Process Digital Cover Photography images of tree canopies to get canopy attributes like Foliage Cover and Leaf Area Index. Detailed description of the methods in Chianucci et al. (2022) <doi:10.1007/s00468-018-1666-3>.

This package provides a collection of functions to pre-process amplification curve data from polymerase chain reaction (PCR) or isothermal amplification reactions. Contains functions to normalize and baseline amplification curves, to detect both the start and end of an amplification reaction, several smoothers (e.g., LOWESS, moving average, cubic splines, Savitzky-Golay), a function to detect false positive amplification reactions and a function to determine the amplification efficiency. Quantification point (Cq) methods include the first (FDM) and second approximate derivative maximum (SDM) methods (calculated by a 5-point-stencil) and the cycle threshold method. Data sets of experimental nucleic acid amplification systems ('VideoScan HCU', capillary convective PCR (ccPCR)) and commercial systems are included. Amplification curves were generated by helicase dependent amplification (HDA), ccPCR or PCR. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used. For more information see: Roediger et al. (2015) <doi:10.1093/bioinformatics/btv205>.

Generate fully interactive and dynamic funnel plots and statistical process control ('SPC') charts. All data manipulation, calculation, and plotting is done in JavaScript', allowing for completely dynamic charts without the need for a Shiny server. For more details see Spiegelhalter (2004) <doi:10.1002/sim.1970> and Pfadt & Wheeler (1995) <doi:10.1901/jaba.1995.28-349>.

The Cauchy Process can model pulsed continuous trait evolution on phylogenies. The likelihood is tractable, and is used for parameter inference and ancestral trait reconstruction. See Bastide and Didier (2023) <doi:10.1093/sysbio/syad053>.