This package provides functions and data are provided that support a course that emphasizes statistical issues of inference and generalizability. The functions are designed to make it straightforward to illustrate the use of cross-validation, the training/test approach, simulation, and model-based estimates of accuracy. Methods considered are Generalized Additive Modeling, Linear and Quadratic Discriminant Analysis, Tree-based methods, and Random Forests.

Performing goodness-of-fit tests for stochastic block models used to fit network data. Among the three variants discussed in Karwa et al. (2023) <doi:10.1093/jrsssb/qkad084>, goodness-of-fit test has been performed for the Erdos-Renyi (ER) and Beta versions.

Likelihood inference in Gaussian copula marginal regression models.

At Novartis, we aimed at standardizing the set of diagnostic plots used for modeling activities in order to reduce the overall effort required for generating such plots. For this, we developed a guidance that proposes an adequate set of diagnostics and a toolbox, called ggPMX to execute them. ggPMX is a toolbox that can generate all diagnostic plots at a quality sufficient for publication and submissions using few lines of code. This package focuses on plots recommended by ISoP <doi:10.1002/psp4.12161>. While not required, you can get/install the R lixoftConnectors package in the Monolix installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When lixoftConnectors is available, R can use Monolix directly to create the required Chart Data instead of exporting it from the Monolix gui.

This package provides an R interface to the GeoServer REST API, allowing to upload and publish data in a GeoServer web-application and expose data to OGC Web-Services. The package currently supports all CRUD (Create,Read,Update,Delete) operations on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes, layers, styles, as well as vector data upload operations. For more information about the GeoServer REST API, see <https://docs.geoserver.org/stable/en/user/rest/>.

Identifies gendered words and phrases in text using a built in dictionary of more than two hundred gendered terms paired with gender neutral alternatives. Reports the share of gendered language in a text, lists every gendered term found together with its suggested neutral replacement, and can rewrite a text in gender neutral form. Plain text files are read with base R, while other document formats such as PDF and Word are supported through the optional readtext package. The dictionary is informed by published guidance on gender inclusive language, including the United Nations guidelines <https://www.un.org/en/gender-inclusive-language/> and the European Parliament guidance on gender neutral language.

This package provides ggplot2 extensions for creating dice-based visualizations where each dot position represents a specific categorical variable. The package includes geom_dice() for displaying presence/absence of categorical variables using traditional dice patterns. Each dice position (1-6) represents a different category, with dots shown only when that category is present. This allows intuitive visualization of up to 6 categorical variables simultaneously.

This package implements three nonparametric two-sample tests for multivariate paired data and pair matching. Methods are described in the associated preprint: <doi:10.48550/arXiv.2007.01497>.

Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.

This package provides a collection of functions to perform Gaussian quadrature with different weight functions corresponding to the orthogonal polynomials in package orthopolynom. Examples verify the orthogonality and inner products of the polynomials.

To aggregate a hyper data frame, defined in the package spatstat.geom', according to a grouping structure. To facilitate downstream analysis based on a "grouped" hyper data frame. The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications. Backward compatibility with user-code published in <doi:10.1093/bioinformatics/btaf430> is not maintained in versions >= 0.4.0 of this package. The authors of those publications are the appropriate contacts for reproducibility inquiries.

Assists in the plotting and functional smoothing of traits measured over time and the extraction of features from these traits, implementing the SET (Smoothing and Extraction of Traits) method described in Brien et al. (2020) Plant Methods, 16. Smoothing of growth trends for individual plants using natural cubic smoothing splines or P-splines is available for removing transient effects and segmented smoothing is available to deal with discontinuities in growth trends. There are graphical tools for assessing the adequacy of trait smoothing, both when using this and other packages, such as those that fit nonlinear growth models. A range of per-unit (plant, pot, plot) growth traits or features can be extracted from the data, including single time points, interval growth rates and other growth statistics, such as maximum growth or days to maximum growth. The package also has tools adapted to inputting data from high-throughput phenotyping facilities, such from a Lemna-Tec Scananalyzer 3D (see <https://www.youtube.com/watch?v=MRAF_mAEa7E/> for more information). The package growthPheno can also be installed from <http://chris.brien.name/rpackages/>.

Create graticule lines and labels for maps. Control the creation of lines or tiles by setting their placement (at particular meridians and parallels) and extent (along parallels and meridians). Labels are created independently of lines.

This package provides functions that automate downloading and importing University of East Anglia Climate Research Unit ('CRU') CL v. 2.0 climatology data, facilitates the calculation of minimum temperature and maximum temperature and formats the data into a data.table object or a terra SpatRaster object. CRU CL v. 2.0 data are a gridded climatology of 1961-1990 monthly means released in 2002 and cover all land areas (excluding Antarctica) at 10 arc minutes (0.1666667 degree) resolution. For more information see the description of the data provided by the University of East Anglia Climate Research Unit, <https://crudata.uea.ac.uk/cru/data/hrg/tmc/readme.txt>.

This package provides tools for interacting with the geographic name resolution service ('GNRS') API <https://github.com/ojalaquellueva/gnrs> and associated functionality. The GNRS is a batch application for resolving & standardizing political division names against standard name in the geonames database <http://www.geonames.org/>. The GNRS resolves political division names at three levels: country, state/province and county/parish. Resolution is performed in a series of steps, beginning with direct matching to standard names, followed by direct matching to alternate names in different languages, followed by direct matching to standard codes (such as ISO and FIPS codes). If direct matching fails, the GNRS attempts to match to standard and then alternate names using fuzzy matching, but does not perform fuzzing matching of political division codes. The GNRS works down the political division hierarchy, stopping at the current level if all matches fail. In other words, if a country cannot be matched, the GNRS does not attempt to match state or county.

Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2017) "Optimal Bayesian estimators for latent variable cluster models" <DOI:10.1007/s11222-017-9786-y>.

This package provides a ggplot2 extension for visualising uncertainty with the goal of signal suppression. Usually, uncertainty visualisation focuses on expressing uncertainty as a distribution or probability, whereas ggdibbler differentiates itself by viewing an uncertainty visualisation as an adjustment to an existing graphic that incorporates the inherent uncertainty in the estimates. You provide the code for an existing plot, but replace any of the variables with a vector of distributions, and it will convert the visualisation into it's signal suppression counterpart.

Automated model selection and model-averaging. Provides a wrapper for glm and other functions, automatically generating all possible models (under constraints set by the user) with the specified response and explanatory variables, and finding the best models in terms of some Information Criterion (AIC, AICc or BIC). Can handle very large numbers of candidate models. Features a Genetic Algorithm to find the best models when an exhaustive screening of the candidates is not feasible.

Allows user to choose/gate a region on the plot and returns points within it.

This package provides efficient geospatial thinning algorithms to reduce the density of coordinate data while maintaining spatial relationships. Implements K-D Tree and brute-force distance-based thinning, as well as grid-based and precision-based thinning methods. For more information on the methods, see Elseberg et al. (2012) <https://hdl.handle.net/10446/86202>.

This is an add on package to GAMLSS. The purpose of this package is to allow users to defined truncated distributions in GAMLSS models. The main function gen.trun() generates truncated version of an existing GAMLSS family distribution.

Add mean comparison annotations to a ggplot'. This package provides an easy way to indicate if two or more groups are significantly different in a ggplot'. Usually you do not need to specify the test method, you only need to tell stat_compare() whether you want to perform a parametric test or a nonparametric test, and stat_compare() will automatically choose the appropriate test method based on your data. For comparisons between two groups, the p-value is calculated by t-test (parametric) or Wilcoxon rank sum test (nonparametric). For comparisons among more than two groups, the p-value is calculated by One-way ANOVA (parametric) or Kruskal-Wallis test (nonparametric).

This package provides classes and functions to calculate various distance measures and routes in heterogeneous geographic spaces represented as grids. The package implements measures to model dispersal histories first presented by van Etten and Hijmans (2010) <doi:10.1371/journal.pone.0012060>. Least-cost distances as well as more complex distances based on (constrained) random walks can be calculated. The distances implemented in the package are used in geographical genetics, accessibility indicators, and may also have applications in other fields of geospatial analysis.

This function performs genomic prediction of cross performance using genotype and phenotype data. It processes data in several steps including loading necessary software, converting genotype data, processing phenotype data, fitting mixed models, and predicting cross performance based on weighted marker effects. For more information, see Labroo et al. (2023) <doi:10.1007/s00122-023-04377-z>.