End-member modelling analysis of grain-size data is an approach to unmix a data set's underlying distributions and their contribution to the data set. EMMAgeo provides deterministic and robust protocols for that purpose.

Computes the probability density and cumulative distribution functions of fourteen distributions used for the probabilistic hazard assessment. Estimates the model parameters of the distributions using the maximum likelihood and reports the goodness-of-fit statistics. The recurrence interval estimations of earthquakes are computed for each distribution.

Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data using the model described in Forcina et al. (2012), as extension of the model proposed in Brown and Payne (1986). Allows incorporation of covariates. References: Brown, P. and Payne, C. (1986). Aggregate data, ecological regression and voting transitions''. Journal of the American Statistical Association, 81, 453â 460. <DOI:10.1080/01621459.1986.10478290>. Forcina, A., Gnaldi, M. and Bracalente, B. (2012). A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy''. Statistical Methods & Applications, 21, 109â 119. <DOI:10.1007/s10260-011-0184-x>.

This package implements species distribution modeling and ecological niche modeling, including: bias correction, spatial cross-validation, model evaluation, raster interpolation, biotic "velocity" (speed and direction of movement of a "mass" represented by a raster), interpolating across a time series of rasters, and use of spatially imprecise records. The heart of the package is a set of "training" functions which automatically optimize model complexity based number of available occurrences. These algorithms include MaxEnt, MaxNet, boosted regression trees/gradient boosting machines, generalized additive models, generalized linear models, natural splines, and random forests. To enhance interoperability with other modeling packages, no new classes are created. The package works with PROJ6 geodetic objects and coordinate reference systems.

Computes the Extended Chen-Poisson (ecp) distribution, survival, density, hazard, cumulative hazard and quantile functions. It also allows to generate a pseudo-random sample from this distribution. The corresponding graphics are available. Functions to obtain measures of skewness and kurtosis, k-th raw moments, conditional k-th moments and mean residual life function were added. For details about ecp distribution, see Sousa-Ferreira, I., Abreu, A.M. & Rocha, C. (2023). <doi:10.57805/revstat.v21i2.405>.

Get high-resolution (1 km) daily climate data (precipitation, minimum and maximum temperatures) for points and polygons within Europe.

This package implements comprehensive test data engineering methods as described in Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for engineering and processing test data: Classical Test Theory (CTT) with reliability coefficients for continuous ability assessment; Item Response Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank Analysis (LRA) for ordinal clustering with automatic determination of cluster numbers; Biclustering methods including infinite relational models for simultaneous clustering of examinees and items without predefined cluster numbers; and Bayesian Network Models (BNM) for visualizing inter-item dependencies. Features local dependence analysis through LRA and biclustering, parameter estimation, dimensionality assessment, and network structure visualization for educational, psychological, and social science research.

An extension of knitr that adds flexibility in several ways. One common source of frustration with knitr is that it assumes the directory where the source file lives should be the working directory, which is often not true. ezknitr addresses this problem by giving you complete control over where all the inputs and outputs are, and adds several other convenient features to make rendering markdown/HTML documents easier.

This package provides tools for automatic model selection and diagnostics for Climate and Environmental data. In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models. The envcpt() function should be your first port of call.

Digital simulation of electrochemical processes. Each function allows for implicit and explicit solution of the differential equation using methods like Euler, Backwards implicit, Runge Kutta 4, Crank Nicholson and Backward differentiation formula as well as different number of points for derivative approximation. Several electrochemical processes can be simulated such as: Chronoamperometry, Potential Step, Linear Sweep, Cyclic Voltammetry, Cyclic Voltammetry with electrochemical reaction followed by chemical reaction (EC mechanism) and CV with two following electrochemical reaction (EE mechanism). In update 1.1.0 has been added a general purpose CV function that allow to simulate up to 4 EE mechanism combined with chemical reaction for each species.Update 1.2.0 improved the accuracy of the measurements and allow personalized data resolution for simulation. Bibliography regarding this methods can be found in the following texts. Dieter Britz, Jorg Strutwolf (2016) <ISBN:978-3-319-30292-8>. Allen J. Bard, Larry R. Faulkner (2000) <ISBN:978-0-471-04372-0>.

The 2-D spatial and temporal Epidemic Type Aftershock Sequence ('ETAS') Model is widely used to decluster earthquake data catalogs. Usually, the calculation of standard errors of the ETAS model parameter estimates is based on the Hessian matrix derived from the log-likelihood function of the fitted model. However, when an ETAS model is fitted to a local data set over a time period that is limited or short, the standard errors based on the Hessian matrix may be inaccurate. It follows that the asymptotic confidence intervals for parameters may not always be reliable. As an alternative, this package allows for the construction of bootstrap confidence intervals based on empirical quantiles for the parameters of the 2-D spatial and temporal ETAS model. This version improves on Version 0.1.0 of the package by enabling the study space window (renamed study region') to be polygonal rather than merely rectangular. A Japan earthquake data catalog is used in a second example to illustrate this new feature.

Runs an evolutionary algorithm using the AlphaSimR machinery <doi:10.1093/g3journal/jkaa017> .

Evaluates the empirical characteristic function of univariate and multivariate samples. This package uses RcppArmadillo for fast evaluation. It is also possible to export the code to be used in other packages at C++ level.

Fits extreme value mixture models, which are models for tails not requiring selection of a threshold, for continuous data. It includes functions for model comparison, estimation of quantity of interest in extreme value analysis and plotting. Reference: CN Behrens, HF Lopes, D Gamerman (2004) <doi:10.1191/1471082X04st075oa>. FF do Nascimento, D. Gamerman, HF Lopes <doi:10.1007/s11222-011-9270-z>.

Highest averages & largest remainders allocating seats methods and several party system scores. Implemented highest averages allocating seats methods are D'Hondt, Webster, Danish, Imperiali, Hill-Huntington, Dean, Modified Sainte-Lague, equal proportions and Adams. Implemented largest remainders allocating seats methods are Hare, Droop, Hangenbach-Bischoff, Imperial, modified Imperial and quotas & remainders. The main advantage of this package is that ties are always reported and not incorrectly allocated. Party system scores provided are competitiveness, concentration, effective number of parties, party nationalization score, party system nationalization score and volatility. References: Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>. Norris (2004, ISBN:0-521-82977-1). Laakso & Taagepera (1979) <https://escholarship.org/uc/item/703827nv>. Jones & Mainwaring (2003) <https://kellogg.nd.edu/sites/default/files/old_files/documents/304_0.pdf>. Pedersen (1979) <https://janda.org/c24/Readings/Pedersen/Pedersen.htm>. Golosov (2010) <doi:10.1177/1354068809339538>. Golosov (2014) <doi:10.1177/1354068814549342>.

This package provides methods to simulate and analyse the size and length of branching processes with an arbitrary offspring distribution. These can be used, for example, to analyse the distribution of chain sizes or length of infectious disease outbreaks, as discussed in Farrington et al. (2003) <doi:10.1093/biostatistics/4.2.279>.

Drafting an epidemiological report in Microsoft Word format for a given disease, similar to the Annual Epidemiological Reports published by the European Centre for Disease Prevention and Control. Through standalone functions, it is specifically designed to generate each disease specific output presented in these reports and includes: - Table with the distribution of cases by Member State over the last five years; - Seasonality plot with the distribution of cases at the European Union / European Economic Area level, by month, over the past five years; - Trend plot with the trend and number of cases at the European Union / European Economic Area level, by month, over the past five years; - Age and gender bar graph with the distribution of cases at the European Union / European Economic Area level. Two types of datasets can be used: - The default dataset of dengue 2015-2019 data; - Any dataset specified as described in the vignette.

Analyzes and quantifies ecosystem multifunctionality with functions to calculate multifunctionality richness (MFric), multifunctionality divergence (MFdiv), and multifunctionality regularity (MFreg). These indices help assess the relationship between biodiversity and multiple ecosystem functions. For more details, see Byrnes et al. (2014) <doi:10.1111/2041-210X.12143> and Chao et al. (2024) <doi:10.1111/ele.14336>.

This package provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.

Perform analysis of variance and other important complementary analyses. The functions are easy to use. Performs analysis in various designs, with balanced and unbalanced data.

Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.

Second and backward-incompatible version of R package eodhd <https://eodhd.com/>, extended with a cache and quota system, also offering functions for cleaning and aggregating the financial data.

Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.

This package provides simple functions to create constraints for small test assembly problems (e.g. van der Linden (2005, ISBN: 978-0-387-29054-6)) using sparse matrices. Currently, GLPK', lpSolve', Symphony', and Gurobi are supported as solvers. The gurobi package is not available from any mainstream repository; see <https://www.gurobi.com/downloads/>.