Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) The its.analysis R package - Modelling short time series data <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3398189> for an overview of the method.

Fits a double logistic function to NDVI time series and calculates instantaneous rate of green (IRG) according to methods described in Bischoff et al. (2012) <doi:10.1086/667590>.

This package provides a set of tools for evaluating several measures of case influence for structural equation models.

Non-parametric resampling-based inference tests for ExPosition.

This package provides tools to extract information from the Intergovernmental Organizations ('IGO') Database (v3), provided by the Correlates of War Project <https://correlatesofwar.org/>. See also Pevehouse, J. C. et al. (2020) <doi:10.1177/0022343319881175>.

Plots the conditional coefficients ("marginal effects") of variables included in multiplicative interaction terms.

This package provides an up-to-date version of the InvaCost database (<doi:10.6084/m9.figshare.12668570>) in R, and several functions to analyse the costs of invasive alien species (<doi:10.1111/2041-210X.13929>).

Send emails using the mailgun api. To use this package you will need an account from <https://www.mailgun.com> .

The function install_load checks the local R library(ies) to see if the required package(s) is/are installed or not. If the package(s) is/are not installed, then the package(s) will be installed along with the required dependency(ies). This function pulls source or binary packages from the Posit/RStudio-sponsored CRAN mirror. Lastly, the chosen package(s) is/are loaded. The function load_package simply loads the provided package(s). If this package does not fit your needs, then you may want to consider these other R packages: needs', easypackages', pacman', pak', anyLib', and/or librarian'.

Volume prediction is one of challenging task in forestry research. This package is a comprehensive toolset designed for the fitting and validation of various linear and nonlinear allometric equations (Linear, Log-Linear, Inverse, Quadratic, Cubic, Compound, Power and Exponential) used in the prediction of conifer tree volume. This package is particularly useful for forestry professionals, researchers, and resource managers engaged in assessing and estimating the volume of coniferous trees. This package has been developed using the algorithm of Sharma et al. (2017) <doi:10.13140/RG.2.2.33786.62407>.

This package provides ability to create color palettes from image files. It offers control over the type of color palette to derive from an image (qualitative, sequential or divergent) and other palette properties. Quantiles of an image color distribution can be trimmed. Near-black or near-white colors can be trimmed in RGB color space independent of trimming brightness or saturation distributions in HSV color space. Creating sequential palettes also offers control over the order of HSV color dimensions to sort by. This package differs from other related packages like RImagePalette in approaches to quantizing and extracting colors in images to assemble color palettes and the level of user control over palettes construction.

This package provides fast application of image filters to data matrices, using R and C++ algorithms.

Decomposition of income inequality by groups formed of individuals possessing similar characteristics (e.g., sex, education, age) and their income sources at the same time. Decomposition of the Theil index is based on Giammatteo, M. (2007) <https://www.lisdatacenter.org/wps/liswps/466.pdf>. Decomposition of the squared coefficient of variation is based on Garcia-Penalosa, C., & Orgiazzi, E. (2013) <doi:10.1111/roiw.12054>.

Utilities to work with data from the Internal Displacement Monitoring Centre (IDMC) (<https://www.internal-displacement.org/>), with convenient functions for loading events data from the IDMC API and transforming events data to daily displacement estimates.

Manage a GitHub problem using R: wrangle issues, labels and milestones. It includes functions for storing, prioritizing (sorting), displaying, adding, deleting, and selecting (filtering) issues based on qualitative and quantitative information. Issues (labels and milestones) are written in lists and categorized into the S3 class to be easily manipulated as datasets in R.

This package provides a personalized dynamic latent factor model (Zhang et al. (2024) <doi:10.1093/biomet/asae015>) for irregular multi-resolution time series data, to interpolate unsampled measurements from low-resolution time series.

Some interpolation methods taken from Boost': barycentric rational interpolation, modified Akima interpolation, PCHIP (piecewise cubic Hermite interpolating polynomial) interpolation, and Catmull-Rom splines.

This package provides a set of fast, chainable image-processing operations which are applicable to images of two, three or four dimensions, particularly medical images.

Given two unbiased samples of patient level data on cost and effectiveness for a pair of treatments, make head-to-head treatment comparisons by (i) generating the bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with specified confidence fraction within [0.50, 0.99] that is equivariant with respect to changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge with economic preferences from an ICE map with specified values of lambda, beta and gamma parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves from an ICE map with specified values of lambda, beta and either gamma or eta parameters.

This package provides datasets for the book "Introduction to Statistical Data Analysis for the Life Sciences, Second edition" by Ekstrøm and Sørensen (2014).

Collect marketing data from Instagram Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

Computes individual causes of death and population cause-specific mortality fractions using the InSilicoVA algorithm from McCormick et al. (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used InterVA method. This package provides general model fitting and customization for InSilicoVA algorithm and basic graphical visualization of the output.

Implementation of some of the formulations for the thermodynamic and transport properties released by the International Association for the Properties of Water and Steam (IAPWS). More specifically, the releases R1-76(2014), R5-85(1994), R6-95(2018), R7-97(2012), R8-97, R9-97, R10-06(2009), R11-24, R12-08, R15-11, R16-17(2018), R17-20 and R18-21 at <https://iapws.org>.

This package provides composable invertible transforms for (sparse) matrices.