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 package provides a generalized method to predict and report gender from Brazilian first names using the Brazilian Institute of Geography and Statistics Census data and neural networks.

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 the necessary functions to identify and extract a selection of already available barcode constructs (Cornils, K. et al. (2014) <doi:10.1093/nar/gku081>) and freely choosable barcode designs from next generation sequence (NGS) data. Furthermore, it offers the possibility to account for sequence errors, the calculation of barcode similarities and provides a variety of visualisation tools (Thielecke, L. et al. (2017) <doi:10.1038/srep43249>).

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.

To calculate the relative risk (RR) for the generalized additive model.

This package implements functions and instruments for regression model building and its application to forecasting. The main scope of the package is in variables selection and models specification for cases of time series data. This includes promotional modelling, selection between different dynamic regressions with non-standard distributions of errors, selection based on cross validation, solutions to the fat regression model problem and more. Models developed in the package are tailored specifically for forecasting purposes. So as a results there are several methods that allow producing forecasts from these models and visualising them.

Genomic selection is a specialized form of marker assisted selection. The package contains functions to select important genetic markers and predict phenotype on the basis of fitted training data using integrated model framework (Guha Majumdar et. al. (2019) <doi:10.1089/cmb.2019.0223>) developed by combining one additive (sparse additive models by Ravikumar et. al. (2009) <doi:10.1111/j.1467-9868.2009.00718.x>) and one non-additive (hsic lasso by Yamada et. al. (2014) <doi:10.1162/NECO_a_00537>) model.

Fits generalized additive models for the location, scale and shape parameters of a generalized extreme value response distribution. The methodology is based on Rigby, R.A. and Stasinopoulos, D.M. (2005), <doi:10.1111/j.1467-9876.2005.00510.x> and implemented using functions from the gamlss package <doi:10.32614/CRAN.package.gamlss>.

Offers a generalization of the scatterplot matrix based on the recognition that most datasets include both categorical and quantitative information. Traditional grids of scatterplots often obscure important features of the data when one or more variables are categorical but coded as numerical. The generalized pairs plot offers a range of displays of paired combinations of categorical and quantitative variables. Emerson et al. (2013) <DOI:10.1080/10618600.2012.694762>.

This package provides tools and methods to apply the model Geospatial Regression Equation for European Nutrient losses (GREEN); Grizzetti et al. (2005) <doi:10.1016/j.jhydrol.2004.07.036>; Grizzetti et al. (2008); Grizzetti et al. (2012) <doi:10.1111/j.1365-2486.2011.02576.x>; Grizzetti et al. (2021) <doi:10.1016/j.gloenvcha.2021.102281>.

Estimate natural mortality (M) throughout the life history for organisms, mainly fish and invertebrates, based on gnomonic interval approach proposed by Caddy (1996) <doi:10.1051/alr:1996023> and Martinez-Aguilar et al. (2005) <doi:10.1016/j.fishres.2004.04.008>. It includes estimation of duration of each gnomonic interval (life stage), the constant probability of death (G), and some basic plots.

Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data. Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data. RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular. Reference: Fox, J-P, Veen, D. and Klotzke, K. (2018). Generalized Linear Mixed Models for Randomized Responses. Methodology. <doi:10.1027/1614-2241/a000153>.

An RStudio addin for teaching and learning making plot using the ggplot2 package. You can learn each steps of making plot by clicking your mouse without coding. You can get resultant code for the plot.

This package provides a fully automated workflow for calibrating and analyzing light-level geolocation ('GLS') data from seabirds and other wildlife. The glscalibrator package auto-discovers birds from directory structures, automatically detects calibration periods from the first days of deployment, processes multiple individuals in batch mode, and generates standardized outputs including position estimates, diagnostic plots, and quality control metrics. Implements the established threshold workflow internally, following the methods described in SGAT (Wotherspoon et al. (2016) <https://github.com/SWotherspoon/SGAT>), GeoLight (Lisovski et al. (2012) <doi:10.1111/j.2041-210X.2012.00185.x>), and TwGeos (Lisovski et al. (2019) <https://github.com/slisovski/TwGeos>).

Companion package for the manual guide-R : Guide pour lâ analyse de données dâ enquêtes avec R available at <https://larmarange.github.io/guide-R/>. guideR implements miscellaneous functions introduced in guide-R to facilitate statistical analysis and manipulation of survey data.

This package implements genetic algorithm and particle swarm algorithm for real-valued functions. Various modifications (including hybridization and elitism) of these algorithms are provided. Implemented functions are based on ideas described in S. Katoch, S. Chauhan, V. Kumar (2020) <doi:10.1007/s11042-020-10139-6> and M. Clerc (2012) <https://hal.science/hal-00764996>.

This package provides a simple way to interact with and extract data from the official Google Knowledge Graph API <https://developers.google.com/knowledge-graph/>.

Gene sets are fundamental for gene enrichment analysis. The package geneset enables querying gene sets from public databases including GO (Gene Ontology Consortium. (2004) <doi:10.1093/nar/gkh036>), KEGG (Minoru et al. (2000) <doi:10.1093/nar/28.1.27>), WikiPathway (Marvin et al. (2020) <doi:10.1093/nar/gkaa1024>), MsigDb (Arthur et al. (2015) <doi:10.1016/j.cels.2015.12.004>), Reactome (David et al. (2011) <doi:10.1093/nar/gkq1018>), MeSH (Ish et al. (2014) <doi:10.4103/0019-5413.139827>), DisGeNET (Janet et al. (2017) <doi:10.1093/nar/gkw943>), Disease Ontology (Lynn et al. (2011) <doi:10.1093/nar/gkr972>), Network of Cancer Genes (Dimitra et al. (2019) <doi:10.1186/s13059-018-1612-0>) and COVID-19 (Maxim et al. (2020) <doi:10.21203/rs.3.rs-28582/v1>). Gene sets are stored in the list object which provides data frame of geneset and geneset_name'. The geneset has two columns of term ID and gene ID. The geneset_name has two columns of terms ID and term description.

Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t mixture vector autoregressive models, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function and generalized forecast error variance decomposition. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (2025) <doi:10.1080/07350015.2024.2322090>, Savi Virolainen (in press) <doi:10.1016/j.ecosta.2025.09.003>.

Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty. The algorithms combine the MM approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided.

An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).

Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test if a sample follows Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) <http://www.jstor.org/stable/2958206>.

This package provides implementation of the generic composite similarity measure (GCSM) described in Liu et al. (2020) <doi:10.1016/j.ecoinf.2020.101169>. The implementation is in C++ and uses RcppArmadillo'. Additionally, implementations of the structural similarity (SSIM) and the composite similarity measure based on means, standard deviations, and correlation coefficient (CMSC), are included.