Estimation of model parameters for marked Hawkes process. Accounts for missing data in the estimation of the parameters. Technical details found in (Tucker et al., 2019 <DOI:10.1016/j.spasta.2018.12.004>).

Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).

This package contains statistical methods to analyze graphs, such as graph parameter estimation, model selection based on the Graph Information Criterion, statistical tests to discriminate two or more populations of graphs, correlation between graphs, and clustering of graphs. References: Takahashi et al. (2012) <doi:10.1371/journal.pone.0049949>, Fujita et al. (2017) <doi:10.3389/fnins.2017.00066>, Fujita et al. (2017) <doi:10.1016/j.csda.2016.11.016>, Fujita et al. (2019) <doi:10.1093/comnet/cnz028>.

Design single-case phase, alternation and multiple-baseline experiments, and conduct randomization tests on data gathered by means of such designs, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.

Analysis of risk through liability matrices. Contains a Gibbs sampler for network reconstruction, where only row and column sums of the liabilities matrix as well as some other fixed entries are observed, following the methodology of Gandy&Veraart (2016) <doi:10.1287/mnsc.2016.2546>. It also incorporates models that use a power law distribution on the degree distribution.

This package provides a comprehensive suite of functions designed for constructing and managing ShinyItemAnalysis modules, supplemented with detailed guides, ready-to-use templates, linters, and tests. This package allows developers to seamlessly create and integrate one or more modules into their existing packages or to start a new module project from scratch.

Function for the computation of fractal dimension based on mass of soil particle size distribution by Tyler & Wheatcraft (1992) <doi:10.2136/sssaj1992.03615995005600020005x>. It also provides functions for calculation of mean weight and geometric mean diameter of particle size distribution by Perfect et al. (1992) <doi:10.2136/sssaj1992.03615995005600050012x>.

The implementation to perform the geometric spatial point analysis developed in Hernández & Solàs (2022) <doi:10.1007/s00180-022-01244-1>. It estimates the geometric goodness-of-fit index for a set of variables against a response one based on the sf package. The package has methods to print and plot the results.

This package provides functions and utilities to perform Statistical Analyses in the Six Sigma way. Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control), you can manage several Quality Management studies: Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis, etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2] and "Quality Control with R" [ISBN 978-3-319-24046-6], are also included in the package.

An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See <http://snap.stanford.edu/seismic/> for more information and datasets.

The saemix package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. It (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.

Evaluating the consistency assumption of Network Meta-Analysis both globally and locally in the Bayesian framework. Inconsistencies are located by applying Bayesian variable selection to the inconsistency factors. The implementation of the method is described by Seitidis et al. (2023) <doi:10.1002/sim.9891>.

From output files obtained from the software ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.

This package provides tools developed to facilitate the establishment of the rank and social hierarchy for gregarious animals by the Si method developed by Kondo & Hurnik (1990)<doi:10.1016/0168-1591(90)90125-W>. It is also possible to determine the number of agonistic interactions between two individuals, sociometric and dyadics matrix from dataset obtained through electronic bins. In addition, it is possible plotting the results using a bar plot, box plot, and sociogram.

This package provides a compilation of functions designed to assist users on the correlation analysis of crop yield and soil test values. Functions to estimate crop response patterns to soil nutrient availability and critical soil test values using various approaches such as: 1) the modified arcsine-log calibration curve (Correndo et al. (2017) <doi:10.1071/CP16444>); 2) the graphical Cate-Nelson quadrants analysis (Cate & Nelson (1965)), 3) the statistical Cate-Nelson quadrants analysis (Cate & Nelson (1971) <doi:10.2136/sssaj1971.03615995003500040048x>), 4) the linear-plateau regression (Anderson & Nelson (1975) <doi:10.2307/2529422>), 5) the quadratic-plateau regression (Bullock & Bullock (1994) <doi:10.2134/agronj1994.00021962008600010033x>), and 6) the Mitscherlich-type exponential regression (Melsted & Peck (1977) <doi:10.2134/asaspecpub29.c1>). The package development stemmed from ongoing work with the Fertilizer Recommendation Support Tool (FRST) and Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (SIIL) projects.

This package provides functions related to multivariate measures of independence and ICA: -estimate independent components by minimizing distance covariance; -conduct a test of mutual independence based on distance covariance; -estimate independent components via infomax (a popular method but generally performs poorer than mdcovica, ProDenICA, and/or fastICA, but is useful for comparisons); -order indepedent components by skewness; -match independent components from multiple estimates; -other functions useful in ICA.

Select the most suitable shape to describe the relationship between the exposure and the outcome among increasing, decreasing, convex, and concave shapes (Yin et al. (2021) <DOI:10.1007/s13571-020-00246-7>); estimate the direct and indirect effects with prior knowledge on the relationship between the mediator and the outcome with binary exposure (Yin et al. (2024) <DOI:10.1007/s13571-024-00336-w>); estimate the direct and indirect effects using linear regression-based approach (VanderWeele (2015, ISBN:9780199325870)).

This package provides a toolkit for simulation studies concerning time-to-event endpoints with non-proportional hazards. SimNPH encompasses functions for simulating time-to-event data in various scenarios, simulating different trial designs like fixed-followup, event-driven, and group sequential designs. The package provides functions to calculate the true values of common summary statistics for the implemented scenarios and offers common analysis methods for time-to-event data. Helper functions for running simulations with the SimDesign package and for aggregating and presenting the results are also included. Results of the conducted simulation study are available in the paper: "A Comparison of Statistical Methods for Time-To-Event Analyses in Randomized Controlled Trials Under Non-Proportional Hazards", Klinglmüller et al. (2025) <doi:10.1002/sim.70019>.

We present a rank-based Mercer kernel to compute a pair-wise similarity metric corresponding to informative representation of data. We tailor the development of a kernel to encode our prior knowledge about the data distribution over a probability space. The philosophical concept behind our construction is that objects whose feature values fall on the extreme of that featureâ s probability mass distribution are more similar to each other, than objects whose feature values lie closer to the mean. Semblance emphasizes features whose values lie far away from the mean of their probability distribution. The kernel relies on properties empirically determined from the data and does not assume an underlying distribution. The use of feature ranks on a probability space ensures that Semblance is computational efficacious, robust to outliers, and statistically stable, thus making it widely applicable algorithm for pattern analysis. The output from the kernel is a square, symmetric matrix that gives proximity values between pairs of observations.

This package provides several functions for area level of small area estimation using hierarchical Bayesian (HB) methods with several univariate distributions for variables of interest. The dataset that is used in every function is generated accordingly in the Example. The rjags package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

Bayesian variable selection, model choice, and regularized estimation for (spatial) generalized additive mixed regression models via stochastic search variable selection with spike-and-slab priors.

An algorithm to cluster satellite hot spot data spatially and temporally.

R version of scperturb tool for single-cell perturbation analysis. Contains wrappers for performing E-statistics for Seurat objects. More details on the method can be found in Peidli et al. (2023) <doi:10.1101/2022.08.20.504663> and in Székely and Rizzo (2004).

Create a hexagon tile map display from spatial polygons. Each polygon is represented by a hexagon tile, placed as close to it's original centroid as possible, with a focus on maintaining spatial relationship to a focal point. Developed to aid visualisation and analysis of spatial distributions across Australia, which can be challenging due to the concentration of the population on the coast and wide open interior.