This package implements parametric modal Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) models utilizing the Skewed Distribution (SKD) family, in which the conditional mode, rather than the conditional mean, follows the (seasonal) ARIMA recursion. Current distributions supported are the Skew-Normal, Skewed Student-t, and Skewed Laplace. The parameters are estimated by maximum likelihood using analytical gradients. Includes residual diagnostics, simulation envelopes, automatic order selection, joint and marginal modal forecasts, exact and parametric bootstrap prediction intervals, and classical asymptotic inference via the Fisher Information matrix. Methods are described in Galarza, C.E., Lachos, V.H., Cabral, C.R.B., & Castro, L.M. (2017) <doi:10.1002/sta4.140>.

The current version (0.3.0) streamlines the underlying code, adds a feasibility check to maicWt and maxessWt', extends exmWt.2ipd with options target.ipd and method for one-sided MAIC weighting between two IPDs, and adds wtTrtDiff for the weighted treatment-effect difference with a Wald confidence interval based on Section 5 of Glimm & Yau (2026). The second version (0.2.0) contains implementation for exact matching which is an alternative to propensity score matching (see Glimm & Yau (2026) <doi:10.1080/19466315.2025.2507378>). The initial version (0.1.2) contains a collection of easy-to-implement tools for checking whether a MAIC can be conducted, as well as an alternative way of calculating weights (see Glimm & Yau (2022) <doi:10.1002/pst.2210>.).

Download data from the Ada and Archibald MacLeish Field Station in Whately, MA. The Ada and Archibald MacLeish Field Station is a 260-acre patchwork of forest and farmland located in West Whately, MA that provides opportunities for faculty and students to pursue environmental research, outdoor education, and low-impact recreation (see <https://www.smith.edu/discover-smith/smith-action/sustainable-smith/macleish-field-station> for more information). This package contains weather data over several years, and spatial data on various man-made and natural structures.

Asymptotic efficient closed-form estimators (MLEces) are provided in this package for three multivariate distributions(gamma, Weibull and Dirichlet) whose maximum likelihood estimators (MLEs) are not in closed forms. Closed-form estimators are strong consistent, and have the similar asymptotic normal distribution like MLEs. But the calculation of MLEces are much faster than the corresponding MLEs. Further details and explanations of MLEces can be found in. Jang, et al. (2023) <doi:10.1111/stan.12299>. Kim, et al. (2023) <doi:10.1080/03610926.2023.2179880>.

Computes martingale difference correlation (MDC), martingale difference divergence, and their partial extensions to assess conditional mean dependence. The methods are based on Shao and Zhang (2014) <doi:10.1080/01621459.2014.887012>. Additionally, introduces a novel hypothesis test for evaluating covariate effects on the cure rate in mixture cure models, using MDC-based statistics. The methodology is described in Monroy-Castillo et al. (2025, manuscript submitted).

Framework for the simulation framework for the simulation of complex breeding programs and compare their economic and genetic impact. Associated publication: Pook et al. (2020) <doi:10.1534/g3.120.401193>.

Various tools for microeconomic analysis and microeconomic modelling, e.g. estimating quadratic, Cobb-Douglas and Translog functions, calculating partial derivatives and elasticities of these functions, and calculating Hessian matrices, checking curvature and preparing restrictions for imposing monotonicity of Translog functions.

Lattice functions for drawing folded empirical cumulative distribution plots, or mountain plots. A mountain plot is similar to an empirical CDF plot, except that the curve increases from 0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at the right side. See Monti (1995) <doi:10.1080/00031305.1995.10476179>.

This package contains functions for performing Mokken scale analysis on test and questionnaire data. It includes an automated item selection algorithm, and various checks of model assumptions.

Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <https://matzlab.weebly.com/uploads/7/6/2/2/76229469/mcmc.qpcr.tutorial.v1.2.4.pdf>.

Data class for increased interoperability working with spatial-temporal data together with corresponding functions and methods (conversions, basic calculations and basic data manipulation). The class distinguishes between spatial, temporal and other dimensions to facilitate the development and interoperability of tools build for it. Additional features are name-based addressing of data and internal consistency checks (e.g. checking for the right data order in calculations).

Calculate various indices, like Crude Migration Rate, different Gini indices or the Coefficient of Variation among others, to show the (un)equality of migration.

Extends the mlr3 ML framework with methods for spatial objects. Data storage and prediction are supported for packages terra', raster and stars'.

The current version of the MixSAL package allows users to generate data from a multivariate SAL distribution or a mixture of multivariate SAL distributions, evaluate the probability density function of a multivariate SAL distribution or a mixture of multivariate SAL distributions, and fit a mixture of multivariate SAL distributions using the Expectation-Maximization (EM) algorithm (see Franczak et. al, 2014, <doi:10.1109/TPAMI.2013.216>, for details).

This package provides tools for animal movement modelling using hidden Markov models. These include processing of tracking data, fitting hidden Markov models to movement data, visualization of data and fitted model, decoding of the state process, etc. <doi:10.1111/2041-210X.12578>.

Micro simulation model to reproduce natural history of cervical cancer and cost-effectiveness evaluation of prevention strategies. See Georgalis L, de Sanjose S, Esnaola M, Bosch F X, Diaz M (2016) <doi:10.1097/CEJ.0000000000000202> for more details.

This package provides a comprehensive package for comparing multiple Structural Equation Models (SEM). Supports both nested and non-nested model comparisons, chi-square difference tests, and extraction of multiple fit indices including AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), CFI (Comparative Fit Index), TLI (Tucker-Lewis Index), RMSEA (Root Mean Square Error of Approximation), and SRMR (Standardized Root Mean Square Residual). Built on top of the lavaan package for seamless SEM model comparison workflows. The Vuong test (Vuong, 1989) for non-nested models is used as the statistical test.

This is the core package offering a portal to the many packages universe. It includes functions to help researchers access, work across, and maintain ensembles of datasets on global governance called datacubes.

The Washington Metropolitan Area Transit Authority is a government agency operating light rail and passenger buses in the Washington D.C. area. With a free developer account, access their Metro Transparent Data Sets API <https://developer.wmata.com/> to return data frames of transit data for easy analysis.

Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez-Rondon & Dufour (2026a) <doi:10.34989/swp-2026-23> that can be used to identify the number of regimes in Markov switching models. For a detailed description of the package, see Rodriguez-Rondon & Dufour (2026b) <doi:10.34989/swp-2026-7>.

This package provides a collection of functions for computations and visualizations of microbial pan-genomes.

The Macroeconomics-at-Risk (MaR) approach is based on a two-step semi-parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the MaR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.

R interface to POSIX mmap and Window's MapViewOfFile.

This package provides functions for fitting various models to capture-recapture data including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and the multi-variate state model structure for survival estimation and POPAN structured Jolly-Seber models for abundance estimation. There are also Hidden Markov model (HMM) implementations of CJS and multistate models with and without state uncertainty and a simulation capability for HMM models.