Fit models of modularity to morphological landmarks. Perform model selection on results. Fit models with a single within-module correlation or with separate within-module correlations fitted to each module.

This package provides a collection of convenient functions to facilitate common tasks in exploratory data analysis. Some common tasks include generating summary tables of variables, displaying tables as a flextable or a kable and showing distributions of variables using ggplot2'. Labels stating the source file with run time can be easily generated for annotation in tables and plots.

This package provides a set of extensions for the ergm package to fit multilayer/multiplex/multirelational networks and samples of multiple networks. ergm.multi is a part of the Statnet suite of packages for network analysis. See Krivitsky, Koehly, and Marcum (2020) <doi:10.1007/s11336-020-09720-7> and Krivitsky, Coletti, and Hens (2023) <doi:10.1080/01621459.2023.2242627>.

Lactation curve modeling plays a central role in dairy production, supporting management decisions and the selection of animals with superior productivity and resilience. The package EMOTIONS fits 47 models for lactation curves and creates ensemble models using model averaging based on Akaike information criterion, Bayesian information criterion, root mean square percentage error, and mean squared error, variance of the predictions, cosine similarity for each model's predictions, and Bayesian Model Average. The daily production values predicted through the ensemble models can be used to estimate resilience indicators in the package. Additionally, the package allows the graphical visualization of the model ranks and the predicted lactation curves.

This cointegration based Time Delay Neural Network Model hybrid model allows the researcher to make use of the information extracted by the cointegrating vector as an input in the neural network model.

Open source data allows for reproducible research and helps advance our knowledge. The purpose of this package is to collate open source ophthalmic data sets curated for direct use. This is real life data of people with intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF), due to age-related macular degeneration or diabetic macular edema. Associated publications of the data sets: Fu et al. (2020) <doi:10.1001/jamaophthalmol.2020.5044>, Moraes et al (2020) <doi:10.1016/j.ophtha.2020.09.025>, Fasler et al. (2019) <doi:10.1136/bmjopen-2018-027441>, Arpa et al. (2020) <doi:10.1136/bjophthalmol-2020-317161>, Kern et al. 2020, <doi:10.1038/s41433-020-1048-0>.

This package implements comprehensive test data engineering methods as described in Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for engineering and processing test data: Classical Test Theory (CTT) with reliability coefficients for continuous ability assessment; Item Response Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank Analysis (LRA) for ordinal clustering with automatic determination of cluster numbers; Biclustering methods including infinite relational models for simultaneous clustering of examinees and items without predefined cluster numbers; and Bayesian Network Models (BNM) for visualizing inter-item dependencies. Features local dependence analysis through LRA and biclustering, parameter estimation, dimensionality assessment, and network structure visualization for educational, psychological, and social science research.

Allows the user to determine minimum sample sizes that achieve target size and power at a specified alternative. For more information, see â Exact samples sizes for clinical trials subject to size and power constraintsâ by Lloyd, C.J. (2022) Preprint <doi:10.13140/RG.2.2.11828.94085>.

Some wrappers, functions and data sets for for spatial point pattern analysis (mainly based on spatstat'), used in the book "Introduccion al Analisis Espacial de Datos en Ecologia y Ciencias Ambientales: Metodos y Aplicaciones" and in the papers by De la Cruz et al. (2008) <doi:10.1111/j.0906-7590.2008.05299.x> and Olano et al. (2009) <doi:10.1051/forest:2008074>.

Implementation of a function which calculates the empirical excess mass for given \eqn\lambda and given maximal number of modes (excessm()). Offering powerful plot features to visualize empirical excess mass (exmplot()). This includes the possibility of drawing several plots (with different maximal number of modes / cut off values) in a single graph.

This package provides functions to clean and standardize messy data, including textual categories and free-text addresses, using Large Language Models. The package corrects typos, expands abbreviations, and maps inconsistent entries to standardized values. Ideal for Bioinformatics, business, and general data cleaning tasks.

Allows to calculate the probabilities of occurrences of an event in a great number of repetitions of Bernoulli experiment, through the application of the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. Gives the possibility to show the results graphically and analytically, and to compare the results obtained by the application of the above theorems with those calculated by the direct application of the Binomial formula. Is basically useful for educational purposes.

Get high-resolution (1 km) daily climate data (precipitation, minimum and maximum temperatures) for points and polygons within Europe.

This package provides a consistent representation of year-based time scales as a numeric vector with an associated era'. There are built-in era definitions for many year numbering systems used in contemporary and historic calendars (e.g. Common Era, Islamic Hijri years); year-based time scales used in archaeology, astronomy, geology, and other palaeosciences (e.g. Before Present, SI-prefixed annus'); and support for arbitrary user-defined eras. Years can converted from any one era to another using a generalised transformation function. Methods are also provided for robust casting and coercion between years and other numeric types, type-stable arithmetic with years, and pretty-printing in tables.

Compute energy landscapes using a digital elevation model and body mass of animals.

This package provides data sets and R Codes for E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing.

The amplitude-dependent autoregressive time series model (EXPAR) proposed by Haggan and Ozaki (1981) <doi:10.2307/2335819> was improved by incorporating the moving average (MA) framework for capturing the variability efficiently. Parameters of the EXPARMA model can be estimated using this package. The user is provided with the best fitted EXPARMA model for the data set under consideration.

Downloads a satellite image via ESRI and maptiles (these are originally from a variety of aerial photography sources), translates the image into a perceptually uniform color space, runs one of a few different clustering algorithms on the colors in the image searching for a user-supplied number of colors, and returns the resulting color palette.

This package implements a segmentation algorithm for multiple change-point detection in univariate time series using the Ensemble Binary Segmentation of Korkas (2022) <Journal of the Korean Statistical Society, 51(1), pp.65-86.>.

This package provides a set of functions, which facilitates removing objects from an environment. It allows to delete objects specified with regular expression or with other conditions (e.g. if object is numeric), using one function call.

This data management package provides some helper classes for publicly available data sources (HMD, DESTATIS) in Demography. Similar to ideas developed in the Bioconductor project <https://bioconductor.org> we strive to encapsulate data in easy to use S4 objects. If original data is provided in a text file, the resulting S4 object contains all information from that text file. But the information is somehow structured (header, footer, etc). Further the classes provide methods to make a subset for selected calendar years or selected regions. The resulting subset objects still contain the original header and footer information.

This package implements the Bayesian and likelihood methods proposed in Imai, Lu, and Strauss (2008 <doi:10.1093/pan/mpm017>) and (2011 <doi:10.18637/jss.v042.i05>) for ecological inference in 2 by 2 tables as well as the method of bounds introduced by Duncan and Davis (1953). The package fits both parametric and nonparametric models using either the Expectation-Maximization algorithms (for likelihood models) or the Markov chain Monte Carlo algorithms (for Bayesian models). For all models, the individual-level data can be directly incorporated into the estimation whenever such data are available. Along with in-sample and out-of-sample predictions, the package also provides a functionality which allows one to quantify the effect of data aggregation on parameter estimation and hypothesis testing under the parametric likelihood models.

Analysis and visualization of plant disease progress curve data. Functions for fitting two-parameter population dynamics models (exponential, monomolecular, logistic and Gompertz) to proportion data for single or multiple epidemics using either linear or no-linear regression. Statistical and visual outputs are provided to aid in model selection. Synthetic curves can be simulated for any of the models given the parameters. See Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058> for further information on the methods.

Goodness-of-fit tests for selection of r in the r-largest order statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the Generalized Pareto distribution (GPD). Random number generation and density functions for the GEVr distribution. Profile likelihood for return level estimation using the GEVr and Generalized Pareto distributions. P-value adjustments for sequential, multiple testing error control. Non-stationary fitting of GEVr and GPD. Bader, B., Yan, J. & Zhang, X. (2016) <doi:10.1007/s11222-016-9697-3>. Bader, B., Yan, J. & Zhang, X. (2018) <doi:10.1214/17-AOAS1092>.