Analysis of multi environment data of plant breeding experiments following the analyses described in Malosetti, Ribaut, and van Eeuwijk (2013), <doi:10.3389/fphys.2013.00044>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris. Some functions have been created to be used in conjunction with the R package asreml for the ASReml software, which can be obtained upon purchase from VSN international (<https://vsni.co.uk/software/asreml-r/>).

Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is integrated out of the likelihood using the Laplace approximation. The models are fitted via TMB (Template Model Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) <doi:10.18637/jss.v070.i05>).

Testing the mediation effect of multiple SNPs on an outcome through a mediator.

This package implements the sparse clustering methods of Witten and Tibshirani (2010): "A framework for feature selection in clustering"; published in Journal of the American Statistical Association 105(490): 713-726.

Develop spatial interaction models (SIMs). SIMs predict the amount of interaction, for example number of trips per day, between geographic entities representing trip origins and destinations. Contains functions for creating origin-destination datasets from geographic input datasets and calculating movement between origin-destination pairs with constrained, production-constrained, and attraction-constrained models (Wilson 1979) <doi:10.1068/a030001>.

Modeling spatial dependencies in dependent variables, extending traditional spatial regression approaches. It allows for the joint modeling of both the mean and the variance of the dependent variable, incorporating semiparametric effects in both models. Based on generalized additive models (GAM), the package enables the inclusion of non-parametric terms while maintaining the classical theoretical framework of spatial regression. Additionally, it implements the Generalized Spatial Autoregression (GSAR) model, which extends classical methods like logistic Spatial Autoregresive Models (SAR), probit Spatial Autoregresive Models (SAR), and Poisson Spatial Autoregresive Models (SAR), offering greater flexibility in modeling spatial dependencies and significantly improving computational efficiency and the statistical properties of the estimators. Related work includes: a) J.D. Toloza-Delgado, Melo O.O., Cruz N.A. (2024). "Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices". <doi:10.1016/j.spasta.2024.100864>. b) Cruz, N. A., Toloza-Delgado, J. D., Melo, O. O. (2024). "Generalized spatial autoregressive model". <doi:10.48550/arXiv.2412.00945>.

Interactive visualizations of graphs created with the igraph package using a htmlwidgets wrapper for the sigma.js network visualization v2.4.0 <https://www.sigmajs.org/>, enabling to display several thousands of nodes. While several R packages have been developed to interface sigma.js', all were developed for v1.x.x and none have migrated to v2.4.0 nor are they planning to. This package builds upon the sigmaNet package, and users familiar with it will recognize the similar design approach. Two extensions have been added to the classic sigma.js visualizations by overriding the underlying JavaScript code, enabling to draw a frame around node labels, and to display labels on multiple lines by parsing line breaks. Other additional functionalities that did not require overriding sigma.js code include toggling node visibility when clicked using a node attribute and highlighting specific edges. sigma.js is currently preparing a stable release v3.0.0, and this package plans to update to it when it is available.

It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) <doi:10.1007/s00180-016-0691-1>.

Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers.

Copula based Cox proportional hazards models for survival data subject to dependent censoring. This approach does not assume that the parameter defining the copula is known. The dependency parameter is estimated with other finite model parameters by maximizing a Pseudo likelihood function. The cumulative hazard function is estimated via estimating equations derived based on martingale ideas. Available copula functions include Frank, Gumbel and Normal copulas. Only Weibull and lognormal models are allowed for the censoring model, even though any parametric model that satisfies certain identifiability conditions could be used. Implemented methods are described in the article "Copula based Cox proportional hazards models for dependent censoring" by Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>.

Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. The spatPomp package extends pomp to include algorithms taking advantage of the spatial structure in order to assist with handling high dimensional processes. See Asfaw et al. (2024) <doi:10.48550/arXiv.2101.01157> for further description of the package.

The overall performance of soil ecosystem services and productivity greatly relies on soil health, making it a crucial indicator. The evaluation of soil physical, chemical, and biological parameters is necessary to determine the overall soil quality index. In our package, three commonly used methods, including linear scoring, regression-based, and principal component-based soil quality indexing, are employed to calculate the soil quality index. This package has been developed using concept of Bastida et al. (2008) and Doran and Parkin (1994) <doi:10.1016/j.geoderma.2008.08.007> <doi:10.2136/sssaspecpub35.c1>.

Estimation of mean squared prediction error of a small area predictor is provided. In particular, the recent method of Simple, Unified, Monte-Carlo Assisted approach for the mean squared prediction error estimation of small area predictor is provided. We also provide other existing methods of mean squared prediction error estimation such as jackknife method for the mixed logistic model.

This package implements sparse generalized factor models (sparseGFM) for dimension reduction and variable selection in high-dimensional data with automatic adaptation to weak factor scenarios. The package supports multiple data types (continuous, count, binary) through generalized linear model frameworks and handles missing values automatically. It provides 12 different penalty functions including Least Absolute Shrinkage and Selection Operator (Lasso), adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP), group Lasso, and their adaptive versions for inducing row-wise sparsity in factor loadings. Key features include cross-validation for regularization parameter selection using Sparsity Information Criterion (SIC), automatic determination of the number of factors via multiple information criteria, and specialized algorithms for row-sparse loading structures. The methodology employs alternating minimization with Singular Value Decomposition (SVD)-based identifiability constraints and is particularly effective for high-dimensional applications in genomics, economics, and social sciences where interpretable sparse dimension reduction is crucial. For penalty functions, see Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, Fan and Li (2001) <doi:10.1198/016214501753382273>, and Zhang (2010) <doi:10.1214/09-AOS729>.

Generate an invoice containing a header with invoice number and businesses details. The invoice table contains any of: salary, one-liner costs, grouped costs. Under the table signature and bank account details appear. Pages are numbered when more than one. Source .json and .Rmd files are editable in the app. A .csv file with raw data can be downloaded. This package includes functions for getting exchange rates between currencies based on quantmod (Ryan and Ulrich, 2023 <https://CRAN.R-project.org/package=quantmod>).

The SC-SR Algorithm is used to calculate fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible making of the failure cause in a competing risks environment. In the version 2.0 the function creating the probability matrix from double-censored data is added.

Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms.

Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. (2008) <doi:10.1093/biostatistics/kxm054>; Cook, J. R. and Stefanski, L. A. (1994) <doi:10.1080/01621459.1994.10476871>; Little, R. J. A. and Rubin, D. B. (2002, ISBN:978-0-471-18386-0).

Plots a QQ-Norm Plot with several Gaussian simulations.

Implementations for two different Bayesian models of differential co-expression. scdeco.cop() fits the bivariate Gaussian copula model from Zichen Ma, Shannon W. Davis, Yen-Yi Ho (2023) <doi:10.1111/biom.13701>, while scdeco.pg() fits the bivariate Poisson-Gamma model from Zhen Yang, Yen-Yi Ho (2022) <doi:10.1111/biom.13457>.

This package provides an implementation of simultaneous tolerance bounds (STB), useful for checking whether a numeric vector fits to a hypothetical null-distribution or not. Furthermore, there are functions for computing STB (bands, intervals) for random variates of linear mixed models fitted with package VCA'. All kinds of, possibly transformed (studentized, standardized, Pearson-type transformed) random variates (residuals, random effects), can be assessed employing STB-methodology.

Genomic alterations including single nucleotide substitution, copy number alteration, etc. are the major force for cancer initialization and development. Due to the specificity of molecular lesions caused by genomic alterations, we can generate characteristic alteration spectra, called signature (Wang, Shixiang, et al. (2021) <DOI:10.1371/journal.pgen.1009557> & Alexandrov, Ludmil B., et al. (2020) <DOI:10.1038/s41586-020-1943-3> & Steele Christopher D., et al. (2022) <DOI:10.1038/s41586-022-04738-6>). This package helps users to extract, analyze and visualize signatures from genomic alteration records, thus providing new insight into cancer study.

An extension of sensitivity, specificity, positive and negative predictive value to continuous predicted and reference memberships in [0, 1].

Use the R console as an interactive learning environment. Users receive immediate feedback as they are guided through self-paced lessons in data science and R programming.