R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press.

The nonparametric trend and its derivatives in equidistant time series (TS) with short-memory stationary errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. A Nadaraya-Watson kernel smoother is also built-in as a comparison. With version 1.1.0, a linearity test for the trend function, forecasting methods and backtesting approaches are implemented as well. The smoothing methods of the package are described in Feng, Y., Gries, T., and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598>.

This package provides functions to generate K-fold cross validation (CV) folds and CV test error estimates that take into account how a survey dataset's sampling design was constructed (SRS, clustering, stratification, and/or unequal sampling weights). You can input linear and logistic regression models, along with data and a type of survey design in order to get an output that can help you determine which model best fits the data using K-fold cross validation. Our paper on "K-Fold Cross-Validation for Complex Sample Surveys" by Wieczorek, Guerin, and McMahon (2022) <doi:10.1002/sta4.454> explains why differing how we take folds based on survey design is useful.

Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for concentration of infectious units from serial limiting dilution assays. This package uses the likelihood equation, exact goodness of fit p-values, and exact confidence intervals described in Meyers et al. (1994) <http://jcm.asm.org/content/32/3/732.full.pdf>. This software is also implemented as a web application through the Shiny R package <https://iupm.shinyapps.io/sldassay/>.

Animal movement models including Moving-Resting Process with Embedded Brownian Motion (Yan et al., 2014, <doi:10.1007/s10144-013-0428-8>; Pozdnyakov et al., 2017, <doi:10.1007/s11009-017-9547-6>), Brownian Motion with Measurement Error (Pozdnyakov et al., 2014, <doi:10.1890/13-0532.1>), Moving-Resting-Handling Process with Embedded Brownian Motion (Pozdnyakov et al., 2020, <doi:10.1007/s11009-020-09774-1>), Moving-Resting Process with Measurement Error (Hu et al., 2021, <doi:10.1111/2041-210X.13694>), Moving-Moving Process with two Embedded Brownian Motions.

This package provides plotting utilities supporting packages in the easystats ecosystem (<https://github.com/easystats/easystats>) and some extra themes, geoms, and scales for ggplot2'. Color scales are based on <https://materialui.co/>. References: Lüdecke et al. (2021) <doi:10.21105/joss.03393>.

Validate data.frames against schemas to ensure that data matches expectations. Define schemas using tidyselect and predicate functions for type consistency, nullability, and more. Schema failure messages can be tailored for non-technical users and are ideal for user-facing applications such as in shiny or plumber'.

The implementation to perform the geometric spatial point analysis developed in Hernández & Solàs (2022) <doi:10.1007/s00180-022-01244-1>. It estimates the geometric goodness-of-fit index for a set of variables against a response one based on the sf package. The package has methods to print and plot the results.

This package provides a ggplot2 theme and colour palettes to create accessible data visualisations in the Scottish Government.

An Optimization Algorithm Applied to Stratification Problem.This function aims at constructing optimal strata with an optimization algorithm based on a global optimisation technique called Biased Random Key Genetic Algorithms.

This package provides a collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria. Theoretical support for the heuristic search methods and exploratory data analysis criteria is in Cadima, Cerdeira, Minhoto (2003, <doi:10.1016/j.csda.2003.11.001>). Theoretical support for the leap and bounds algorithm and the criteria for the general multivariate linear model is in Duarte Silva (2001, <doi:10.1006/jmva.2000.1920>). There is a package vignette "subselect", which includes additional references.

Routines to write, simulate, and validate stock-flow consistent (SFC) models. The accounting structure of SFC models are described in Godley and Lavoie (2007, ISBN:978-1-137-08599-3). The algorithms implemented to solve the models (Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010) <doi:10.2139/ssrn.1729205> and Peressini and Sullivan (1988, ISBN:0-387-96614-5).

This package provides functions and Datasets from Lohr, S. (1999), Sampling: Design and Analysis, Duxbury.

Scale alignment is a new procedure for rescaling dimensions of between-items multidimensional Rasch family models so that dimensions scores can be compared directly (Feuerstahler & Wilson, 2019; under review) <doi:10.1111/jedm.12209>. This package includes functions for implementing delta-dimensional alignment (DDA) and logistic regression alignment (LRA) for dichotomous or polytomous data. This function also includes a wrapper for models fit using the TAM package.

Interfaces with the SigOpt API. More info at <https://sigopt.com>.

Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals. It is built to perform very well in the presence of significant level shifts. It is designed to play well with any breakpoint algorithm and any smoothing algorithm. Currently defaults to lowess for smoothing and strucchange for breakpoint identification. The package is useful in areas such as trend analysis, time series decomposition, breakpoint identification and anomaly detection.

Bayesian Markov chain Monte Carlo (MCMC) estimation of spatial panel data models including Spatial Autoregressive (SAR), Spatial Durbin Model (SDM), Spatial Error Model (SEM), Spatial Durbin Error Model (SDEM), and Spatial Lag of X (SLX) specifications with fixed effects. Supports convex combinations of multiple spatial weight matrices and Bayesian Model Averaging (BMA) over subsets of weight matrices. Implements the convex combination spatial weight matrix methodology of Debarsy and LeSage (2021) <doi:10.1080/07350015.2020.1840993> and the Bayesian spatial panel data models of LeSage and Pace (2009, ISBN:9781420064247).

This package provides a general-purpose implementation of synthetic control methods that accounts for potential spillover effects between units. Based on the methodology of Cao and Dowd (2019) <doi:10.48550/arXiv.1902.07343> "Estimation and Inference for Synthetic Control Methods with Spillover Effects".

Stop signal task data of go and stop trials is generated per participant. The simulation process is based on the generally non-independent horse race model and fixed stop signal delay or tracking method. Each of go and stop process is assumed having exponentially modified Gaussian(ExG) or Shifted Wald (SW) distributions. The output data can be converted to BEESTS software input data enabling researchers to test and evaluate various brain stopping processes manifested by ExG or SW distributional parameters of interest. Methods are described in: Soltanifar M (2020) <https://hdl.handle.net/1807/101208>, Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013) <doi:10.3389/fpsyg.2013.00918>, Logan GD, Van Zandt T, Verbruggen F, Wagenmakers EJ. (2014) <doi:10.1037/a0035230>.

Decision support tool for prioritizing sites for ecological surveys based on their potential to improve plans for conserving biodiversity (e.g. plans for establishing protected areas). Given a set of sites that could potentially be acquired for conservation management, it can be used to generate and evaluate plans for surveying additional sites. Specifically, plans for ecological surveys can be generated using various conventional approaches (e.g. maximizing expected species richness, geographic coverage, diversity of sampled environmental algorithms. After generating such survey plans, they can be evaluated using conditions) and maximizing value of information. Please note that several functions depend on the Gurobi optimization software (available from <https://www.gurobi.com>). Additionally, the JAGS software (available from <https://mcmc-jags.sourceforge.io/>) is required to fit hierarchical generalized linear models. For further details, see Hanson et al. (2023) <doi:10.1111/1365-2664.14309>.

Send syslog protocol messages to a remote syslog server specified by host name and TCP network port.

Supports the calculation of meteorological characteristics in evapotranspiration research and reference crop evapotranspiration, and offers three models to simulate crop evapotranspiration and soil water balance in the field, including single crop coefficient and dual crop coefficient, as well as the Shuttleworth-Wallace model. These calculations main refer to Allen et al.(1998, ISBN:92-5-104219-5), Teh (2006, ISBN:1-58-112-998-X), and Liu et al.(2006) <doi:10.1016/j.agwat.2006.01.018>.

This package implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using TMB', fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2025) <doi:10.18637/jss.v115.i02>.

Use of Knock Out and Round Robin Techniques in preparing tournament fixtures as discussed in the Book Health and Physical Education by Dr. V K Sharma'(2018,ISBN:978-93-5272-134-4).