For binomial outcome data Alternate Binomial Distributions and Binomial Mixture Distributions are fitted when overdispersion is available.

Estimation, model selection and other aspects of statistical inference in Graphical Gaussian models with edge and vertex symmetries (Graphical Gaussian models with colours). Documentation about gRc is provided in the paper by Hojsgaard and Lauritzen (2007, <doi:10.18637/jss.v023.i06>) and the paper by Hojsgaard and Lauritzen (2008, <doi:10.1111/j.1467-9868.2008.00666.x>).

This package provides functions for performing graphical difference testing. Differences are generated between raster images. Comparisons can be performed between different package versions and between different R versions.

This package provides classes and methods for handling networks or graphs whose nodes are geographical (i.e. locations in the globe). The functionality includes the creation of objects of class geonetwork as a graph with node coordinates, the computation of network measures, the support of spatial operations (projection to different Coordinate Reference Systems, handling of bounding boxes, etc.) and the plotting of the geonetwork object combined with supplementary cartography for spatial representation.

Fits unimodal and multimodal gambin distributions to species-abundance distributions from ecological data, as in in Matthews et al. (2014) <DOI:10.1111/ecog.00861>. gambin is short for gamma-binomial'. The main function is fit_abundances(), which estimates the alpha parameter(s) of the gambin distribution using maximum likelihood. Functions are also provided to generate the gambin distribution and for calculating likelihood statistics.

Set of functions designed to solve inverse problems. The direct problem is used to calculate a cost function to be minimized. Here are listed some papers using Inverse Problems solvers and sensitivity analysis: (Jader Lugon Jr.; Antonio J. Silva Neto 2011) <doi:10.1590/S1678-58782011000400003>. (Jader Lugon Jr.; Antonio J. Silva Neto; Pedro P.G.W. Rodrigues 2008) <doi:10.1080/17415970802082864>. (Jader Lugon Jr.; Antonio J. Silva Neto; Cesar C. Santana 2008) <doi:10.1080/17415970802082922>.

The goal of this package is to translate between different languages without any Google API authentication which is pain and you must pay for the key, This package is free and lightweight.

Plot density and distribution functions with automatic selection of suitable regions. Numerically invert (compute quantiles) distribution functions. Simulate real and complex numbers from distributions of their magnitude and arguments. Optionally, the magnitudes and/or arguments may be fixed in almost arbitrary ways. Create polynomials from roots given in Cartesian or polar form. Small programming utilities: check if an object is identical to NA, count positional arguments in a call, set intersection of more than two sets, check if an argument is unnamed, compute the graph of S4 classes in packages.

Convert the chip ID of GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> to GeneBank Accession and ENTREZID <http://www.ncbi.nlm.nih.gov/gene>.

Fit penalized multivariable linear mixed models with a single random effect to control for population structure in genetic association studies. The goal is to simultaneously fit many genetic variants at the same time, in order to select markers that are independently associated with the response. Can also handle prior annotation information, for example, rare variants, in the form of variable weights. For more information, see the website below and the accompanying paper: Bhatnagar et al., "Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models", 2020, <DOI:10.1371/journal.pgen.1008766>.

Since their introduction by Bose and Nair (1939) <https://www.jstor.org/stable/40383923>, partially balanced incomplete block (PBIB) designs remain an important class of incomplete block designs. The concept of association scheme was used by Bose and Shimamoto (1952) <doi:10.1080/01621459.1952.10501161> for the classification of these designs. The constraint of resources always motivates the experimenter to advance towards PBIB designs, more specifically to higher associate class PBIB designs from balanced incomplete block designs. It is interesting to note that many times higher associate PBIB designs perform better than their counterpart lower associate PBIB designs for the same set of parameters v, b, r, k and lambda_i (i=1,2...m). This package contains functions named GETD() for generating m-associate (m>=2) class PBIB designs along with parameters (v, b, r, k and lambda_i, i = 1, 2,â ¦,m) based on Generalized Triangular (GT) Association Scheme. It also calculates the Information matrix, Average variance factor and canonical efficiency factor of the generated design. These designs, besides having good efficiency, require smaller number of replications and smallest possible concurrence of treatment pairs.

Discrete scales for the colorblind-friendly Okabe-Ito palette, including color', fill', and edge_colour'. ggokabeito provides ggplot2 and ggraph scales to easily use the Okabe-Ito palette in your data visualizations.

Generation of survival data with one (binary) time-dependent covariate. Generation of survival data arising from a progressive illness-death model.

Automates delta log-normal boosted regression tree abundance prediction. Loops through parameters provided (LR (learning rate), TC (tree complexity), BF (bag fraction)), chooses best, simplifies, & generates line, dot & bar plots, & outputs these & predictions & a report, makes predicted abundance maps, and Unrepresentativeness surfaces. Package core built around gbm (gradient boosting machine) functions in dismo (Hijmans, Phillips, Leathwick & Jane Elith, 2020 & ongoing), itself built around gbm (Greenwell, Boehmke, Cunningham & Metcalfe, 2020 & ongoing, originally by Ridgeway). Indebted to Elith/Leathwick/Hastie 2008 Working Guide <doi:10.1111/j.1365-2656.2008.01390.x>; workflow follows Appendix S3. See <https://www.simondedman.com/> for published guides and papers using this package.

This package provides a ggplot2 extension that provides tools for automatically creating scales to focus on subgroups of the data plotted without losing other information.

Simulating single cell RNA-seq data with complicated structure. This package is developed based on the Splat method (Zappia, Phipson and Oshlack (2017) <doi:10.1186/s13059-017-1305-0>). GeneScape incorporates additional features to simulate single cell RNA-seq data with complicated differential expression and correlation structures, such as sub-cell-types, correlated genes (pathway genes) and hub genes.

Estimates the Gini index and computes variances and confidence intervals for finite and infinite populations, using different methods; also computes Gini index for continuous probability distributions, draws samples from continuous probability distributions with Gini indices set by the user; uses Rcpp'. References: Muñoz et al. (2023) <doi:10.1177/00491241231176847>. à lvarez et al. (2021) <doi:10.3390/math9243252>. Giorgi and Gigliarano (2017) <doi:10.1111/joes.12185>. Langel and Tillé (2013) <doi:10.1111/j.1467-985X.2012.01048.x>.

Implementation of several goodness-of-fit tests for functional data. Currently, mostly related with the functional linear model with functional/scalar response and functional/scalar predictor. The package allows for the replication of the data applications considered in Garcà a-Portugués, à lvarez-Liébana, à lvarez-Pérez and González-Manteiga (2021) <doi:10.1111/sjos.12486>.

These Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the Cuba library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002).

To provide a comprehensive analysis of high dimensional longitudinal data,this package provides analysis for any combination of 1) simultaneous variable selection and estimation, 2) mean regression or quantile regression for heterogeneous data, 3) cross-sectional or longitudinal data, 4) balanced or imbalanced data, 5) moderate, high or even ultra-high dimensional data, via computationally efficient implementations of penalized generalized estimating equations.

Spatial data plus the power of the ggplot2 framework means easier mapping when input data are already in the form of spatial objects.

An extension of ggplot2 that makes it easy to add raw grid output, such as customised annotations, to a ggplot2 plot.

Network meta-analyses (mixed treatment comparisons) in the Bayesian framework using JAGS. Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations. van Valkenhoef et al. (2012) <doi:10.1002/jrsm.1054>; van Valkenhoef et al. (2015) <doi:10.1002/jrsm.1167>.

Recursive partitioning based on (generalized) linear mixed models (GLMMs) combining lmer()/glmer() from lme4 and lmtree()/glmtree() from partykit'. The fitting algorithm is described in more detail in Fokkema, Smits, Zeileis, Hothorn & Kelderman (2018; <DOI:10.3758/s13428-017-0971-x>). For detecting and modeling subgroups in growth curves with GLMM trees see Fokkema & Zeileis (2024; <DOI:10.3758/s13428-024-02389-1>).