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.

Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. GWmodel includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.

Simple package to download Google Sheets using just the sharing link. Spreadsheets can be downloaded as a data frame, or as plain text to parse manually. Google Sheets is the new name for Google Docs Spreadsheets <https://www.google.com/sheets/about>.

Core C++ engine for glmbayes': envelope-based iid linear and generalized linear model samplers, prior-family routing, and optional OpenCL acceleration. Sampling for supported non-conjugate models uses accept-reject methods based on likelihood subgradients as in Nygren and Nygren (2006) <doi:10.1198/016214506000000357>. Intended as a developer backend for the glmbayes formula interface; end users should use glmbayes for modelling with interfaces analogous to lm and glm'. Mixed-model engines are planned for a future release.

This package provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution.

Neural networks are applied to create a density value function which approximates density values for a data source. The trained neural network is analyzed for different levels. For each level metric subspaces with density values above a level are determined. The obtained set of metric subspaces and the trained neural network are assembled into a data model. A prerequisite is the definition of a data source, the generation of generative data and the calculation of density values. These tasks are executed using package ganGenerativeData <https://cran.r-project.org/package=ganGenerativeData>.

Solves a least squares system Ax~=b (dim(A)=(m,n) with m >= n) with a precondition matrix B: BAx=Bb (dim(B)=(n,m)). Implemented method is based on GMRES (Saad, Youcef; Schultz, Martin H. (1986). "GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems" <doi:10.1137/0907058>) with callback functions, i.e. no explicit A, B or b are required.

Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models as described in Bilodeau, Lafaye De Micheaux and Mahdi (2015) <doi:10.18637/jss.v065.i01>.

This package implements a new multiple imputation method that draws imputations from a latent joint multivariate normal model which underpins generally structured data. This model is constructed using a sequence of flexible conditional linear models that enables the resulting procedure to be efficiently implemented on high dimensional datasets in practice. See Robbins (2021) <arXiv:2008.02243>.

This package provides a range of filters that can be applied to layers from the ggplot2 package and its extensions, along with other graphic elements such as guides and theme elements. The filters are applied at render time and thus uses the exact pixel dimensions needed.

Make R scripts reproducible, by ensuring that every time a given script is run, the same version of the used packages are loaded (instead of whichever version the user running the script happens to have installed). This is achieved by using the command groundhog.library() instead of the base command library(), and including a date in the call. The date is used to call on the same version of the package every time (the most recent version available at that date). Load packages from CRAN, GitHub, or Gitlab.

Command-line and shiny GUI implementation of the GenEst models for estimating bird and bat mortality at wind and solar power facilities, following Dalthorp, et al. (2018) <doi:10.3133/tm7A2>.

Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile/Accelerated Failure Time (AFT) regression is a competitive flexible model compared to standard/quantile/AFT regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data. References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.

Generates generalized covariate field (GCF) variables from spatial covariates observed at projected coordinates, and selects a stable subset of them for geospatial prediction. For each input covariate the method builds spatial-pattern features (local indicator of spatial association, local Geary's c, log local variance, rank quantile entropy, geocomplexity, log scale variance, local variogram exponent, and signed z-score and median absolute deviation outlier strengths over a series of buffer radii) and neighbourhood-distribution features (buffer-wise quantiles of the covariate values surrounding each location), reduces the buffer and quantile sweeps to a compact set of interpretable functional summaries, and selects variables by random forest importance combined with spatial-block stability resampling and group voting. The GCF method is positioned as prediction-oriented feature construction: its output feeds any downstream regression learner. Methods are described in Song (2026) <doi:10.1080/13658816.2026.2729719>.

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.

Fits multiple-group latent class analysis (LCA) for exploring differences between populations in the data with a multilevel structure. There are two approaches to reflect group differences in glca: fixed-effect LCA (Bandeen-Roche et al (1997) <doi:10.1080/01621459.1997.10473658>; Clogg and Goodman (1985) <doi:10.2307/270847>) and nonparametric random-effect LCA (Vermunt (2003) <doi:10.1111/j.0081-1750.2003.t01-1-00131.x>).

This package provides a collection of palettes and themes for ggplot2', offering a light, pastel aesthetic. Syntax follows the viridis package.

This package provides functions for rendering Bezier curves (Pomax, 2018) <https://pomax.github.io/bezierinfo/> in grid'. There is support for both quadratic and cubic Bezier curves. There are also functions for calculating points on curves, tangents to curves, and normals to curves.

The social network literature features numerous methods for assigning value to paths as a function of their ties. gretel systemizes these approaches, casting them as instances of a generalized path value function indexed by a penalty parameter. The package also calculates probabilistic path value and identifies optimal paths in either value framework. Finally, proximity matrices can be generated in these frameworks that capture high-order connections overlooked in primitive adjacency sociomatrices. Novel methods are described in Buch (2019) <https://davidbuch.github.io/analyzing-networks-with-gretel.html>. More traditional methods are also implemented, as described in Yang, Knoke (2001) <doi:10.1016/S0378-8733(01)00043-0>.

The goal of gnonadd is to simplify workflows in the analysis of non-additive effects of sequence variants. This includes variance effects (Ivarsdottir et. al (2017) <doi:10.1038/ng.3928>), correlation effects, interaction effects and dominance effects. The package also includes convenience functions for visualization.

The name abbreviates "Guide for Rater Agreement under Structural Skew". Generates a Report Card for rater reliability on binary outcomes from an N x k subject-by-rater rating matrix, on both the inter-rater and intra-rater axes. Each panel coefficient is positioned on a data-generating-process-calibrated reference surface conditioned on the study's rater count, sample size, and prevalence, yielding a pooled percentile (the coefficient's position within the design's achievable agreement range) together with a consistency band on panel quality: the quality levels whose sampling distributions are consistent with the observed value at that design. The panel coefficients are the prevalence-adjusted bias-adjusted kappa (PABAK) of Byrt, Bishop, and Carlin (1993) <doi:10.1016/0895-4356(93)90018-V>, the first-order agreement coefficient (AC1) of Gwet (2008) <doi:10.1348/000711006X126600>, the multi-rater kappa of Fleiss (1971) <doi:10.1037/h0031619>, and the observed intraclass correlation. A cross-coefficient discordance diagnostic (delta-hat) reports the spread of the coefficients implied panel qualities and flags panels for which no single coefficient is a stable summary by the spread's percentile on a matched null distribution; for such divergent panels the report routes to a pairwise PABAK matrix and per-rater sensitivity and specificity recovered from the latent-class model of Dawid and Skene (1979) <doi:10.2307/2346806>, with the two-rater bounds of Hui and Walter (1980) <doi:10.2307/2530508>.

In the framework of Symbolic Data Analysis, a relatively new approach to the statistical analysis of multi-valued data, we consider histogram-valued data, i.e., data described by univariate histograms. The methods and the basic statistics for histogram-valued data are mainly based on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric between quantile functions. The package contains unsupervised classification techniques, least square regression and tools for histogram-valued data and for histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi: 10.1007/s11634-014-0176-4>.

Uses support vector machines to identify a perfectly separating hyperplane (linear or curvilinear) between two entities in high-dimensional space. If this plane exists, the entities do not overlap. Applications include overlap detection in morphological, resource or environmental dimensions. More details can be found in: Brown et al. (2020) <doi:10.1111/2041-210X.13363> .

Takes the MinT implementation of the hts'<https://cran.r-project.org/package=hts> package and adapts it to allow degenerate hierarchical structures. Instead of the "nodes" argument, this function takes an S matrix which is more versatile in the structures it allows. For a demo, see Steinmeister and Pauly (2024)<doi:10.15488/17729>. The MinT algorithm is based on Wickramasuriya et al. (2019)<doi:10.1080/01621459.2018.1448825>.