Inference, goodness-of-fit tests, and predictions for continuous and discrete univariate Hidden Markov Models (HMM), including zero-inflated distributions. The goodness-of-fit test is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Nasri et al (2020) <doi:10.1029/2019WR025122>.

Computes experimental designs for a two-arm experiment with covariates via a number of methods: (0) complete randomization and randomization with forced-balance, (1) Greedily optimizing a balance objective function via pairwise switching. This optimization provides lower variance for the treatment effect estimator (and higher power) while preserving a design that is close to complete randomization. We return all iterations of the designs for use in a permutation test, (2) The second is via numerical optimization (via gurobi which must be installed, see <https://www.gurobi.com/documentation/9.1/quickstart_windows/r_ins_the_r_package.html>) a la Bertsimas and Kallus, (3) rerandomization, (4) Karp's method for one covariate, (5) exhaustive enumeration to find the optimal solution (only for small sample sizes), (6) Binary pair matching using the nbpMatching library, (7) Binary pair matching plus design number (1) to further optimize balance, (8) Binary pair matching plus design number (3) to further optimize balance, (9) Hadamard designs, (10) Simultaneous Multiple Kernels. In (1-9) we allow for three objective functions: Mahalanobis distance, Sum of absolute differences standardized and Kernel distances via the kernlab library. This package is the result of a stream of research that can be found in Krieger, A, Azriel, D and Kapelner, A "Nearly Random Designs with Greatly Improved Balance" (2016) <arXiv:1612.02315>, Krieger, A, Azriel, D and Kapelner, A "Better Experimental Design by Hybridizing Binary Matching with Imbalance Optimization" (2021) <arXiv:2012.03330>.

Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) <arXiv:1805.08423v1>.

An extension of ggplot2 for creating complex genomic maps. It builds on the power of ggplot2 and tidyverse adding new ggplot2'-style geoms & positions and dplyr'-style verbs to manipulate the underlying data. It implements a layout concept inspired by ggraph and introduces tracks to bring tidiness to the mess that is genomics data.

Dynamically retrieve data from the web to render HTML tables on inspection in R Markdown HTML documents.

Estimation and inference using the Generalized Maximum Entropy (GME) and Generalized Cross Entropy (GCE) framework, a flexible method for solving ill-posed inverse problems and parameter estimation under uncertainty (Golan, Judge, and Miller (1996, ISBN:978-0471145925) "Maximum Entropy Econometrics: Robust Estimation with Limited Data"). The package includes routines for generalized cross entropy estimation of linear models including the implementation of a GME-GCE two steps approach. Diagnostic tools, and options to incorporate prior information through support and prior distributions are available (Macedo, Cabral, Afreixo, Macedo and Angelelli (2025) <doi:10.1007/978-3-031-97589-9_21>). In particular, support spaces can be defined by the user or be internally computed based on the ridge trace or on the distribution of standardized regression coefficients. Different optimization methods for the objective function can be used. An adaptation of the normalized entropy aggregation (Macedo and Costa (2019) <doi:10.1007/978-3-030-26036-1_2> "Normalized entropy aggregation for inhomogeneous large-scale data") and a two-stage maximum entropy approach for time series regression (Macedo (2022) <doi:10.1080/03610918.2022.2057540>) are also available. Suitable for applications in econometrics, health, signal processing, and other fields requiring robust estimation under data constraints.

Estimates statistically significant marker combination values within which one immunologically distinctive group (i.e., disease case) is more associated than another group (i.e., healthy control), successively, using various combinations (i.e., "gates") of markers to examine features of cells that may be different between groups. For a two-group comparison, the gateR package uses the spatial relative risk function estimated using the sparr package. Details about the sparr package methods can be found in the tutorial: Davies et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More information about relative risk functions using kernel density estimation can be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.

Implementation of a common set of punctual solutions for Cooperative Game Theory.

It analyzes raster maps and other information as input/output files from the Hydrological Distributed Model GEOtop. It contains functions and methods to import maps and other keywords from geotop.inpts file. Some examples with simulation cases of GEOtop 2.x/3.x are presented in the package. Any information about the GEOtop Distributed Hydrological Model can be found in the provided documentation.

The accurate annotation of genes and Quantitative Trait Loci (QTLs) located within candidate markers and/or regions (haplotypes, windows, CNVs, etc) is a crucial step the most common genomic analyses performed in livestock, such as Genome-Wide Association Studies or transcriptomics. The Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package designed to provide an intuitive and straightforward environment to annotate positional candidate genes and QTLs from high-throughput genetic studies in livestock. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies, etc.), and QTL enrichment in different livestock species including cattle, pigs, sheep, and chicken, among others.

Goodness-of-fit tests for skew-normal, gamma, inverse Gaussian, log-normal, Weibull', Frechet', Gumbel, normal, multivariate normal, Cauchy, Laplace or double exponential, exponential and generalized Pareto distributions. Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions.

An optim-style implementation of the Stochastic Quasi-Gradient Differential Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini, and Pierini (2018; <doi:10.1007/978-3-319-72926-8_27>). This optimization algorithm fuses the robustness of the population-based global optimization algorithm "Differential Evolution" with the efficiency of gradient-based optimization. The derivative-free algorithm uses population members to build stochastic gradient estimates, without any additional objective function evaluations. Sala, Baldanzini, and Pierini argue this algorithm is useful for difficult optimization problems under a tight function evaluation budget. This package can run SQG-DE in parallel and sequentially.

Extract and reform data from GWAS (genome-wide association study) results, and then make a single integrated forest plot containing multiple windows of which each shows the result of individual SNPs (or other items of interest).

Likelihood ratio tests for genome-wide association and genome-wide linkage analysis under heterogeneity.

This package provides a set of high efficient functions to decode identifiers of National Football League players.

Flowcharts can be a useful way to visualise complex processes. This package uses the layered grammar of graphics of ggplot2 to create simple flowcharts.

This package performs binary classification via Group Method of Data Handling (GMDH) - type neural network algorithms. There exist two main algorithms available in GMDH() and dceGMDH() functions. GMDH() performs classification via GMDH algorithm for a binary response and returns important variables. dceGMDH() performs classification via diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. Also, the package produces a well-formatted table of descriptives for a binary response. Moreover, it produces confusion matrix, its related statistics and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. All GMDH2 functions are designed for a binary response (Dag et al., 2019, <https://download.atlantis-press.com/article/125911202.pdf>).

Fit joint models of survival and multivariate longitudinal data. The longitudinal data is specified by generalised linear mixed models. The joint models are fit via maximum likelihood using an approximate expectation maximisation algorithm. Bernhardt (2015) <doi:10.1016/j.csda.2014.11.011>.

Derives group sequential clinical trial designs and describes their properties. Particular focus on time-to-event, binary, and continuous outcomes. Largely based on methods described in Jennison, Christopher and Turnbull, Bruce W., 2000, "Group Sequential Methods with Applications to Clinical Trials" ISBN: 0-8493-0316-8.

An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.

This package provides ggplot2 geoms that allow groups of data points to be outlined or highlighted for emphasis. This is particularly useful when working with dense datasets that are prone to overplotting.

Create biplots for GGE (genotype plus genotype-by-environment) and GGB (genotype plus genotype-by-block-of-environments) models. See Laffont et al. (2013) <doi:10.2135/cropsci2013.03.0178>.

Create a grid-based graphviz using the following functions: 1 - Creating the data.frame where the nodes are; 2 - Adding and editing nodes; 3 - Plotting these nodes.

The gap encodes the distance between clusters and improves interpretation of cluster heatmaps. The gaps can be of the same distance based on a height threshold to cut the dendrogram. Another option is to vary the size of gaps based on the distance between clusters.