Work with Ecological Metadata Language ('EML') files. EML is a widely used metadata standard in the ecological and environmental sciences, described in Jones et al. (2006), <doi:10.1146/annurev.ecolsys.37.091305.110031>.

This package provides a C++ implementation of the following evolutionary algorithms: Bat Algorithm (Yang, 2010 <doi:10.1007/978-3-642-12538-6_6>), Cuckoo Search (Yang, 2009 <doi:10.1109/nabic.2009.5393690>), Genetic Algorithms (Holland, 1992, ISBN:978-0262581110), Gravitational Search Algorithm (Rashedi et al., 2009 <doi:10.1016/j.ins.2009.03.004>), Grey Wolf Optimization (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), Harmony Search (Geem et al., 2001 <doi:10.1177/003754970107600201>), Improved Harmony Search (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), Moth-flame Optimization (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>), Particle Swarm Optimization (Kennedy et al., 2001 ISBN:1558605959), Simulated Annealing (Kirkpatrick et al., 1983 <doi:10.1126/science.220.4598.671>), Whale Optimization Algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>). EmiR can be used not only for unconstrained optimization problems, but also in presence of inequality constrains, and variables restricted to be integers.

Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data building on Thomsen (1987) and Park (2008) approaches. References: Park, W.-H. (2008). Ecological Inference and Aggregate Analysis of Election''. PhD Dissertation. University of Michigan. <https://deepblue.lib.umich.edu/bitstream/handle/2027.42/58525/wpark_1.pdf> Thomsen, S.R. (1987, ISBN:87-7335-037-2). Danish Elections 1920 79: a Logit Approach to Ecological Analysis and Inference''. Politica, Aarhus, Denmark.

Some utility functions for validation and data manipulation. These functions can be helpful to reduce internal codes everywhere in package development.

Exploitation, processing and 2D-3D visualization of DICOM-RT files (structures, dosimetry, imagery) for medical physics and clinical research, in a patient-oriented perspective.

If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups. The exterior match is used to compare the two control groups.

This package provides methods and utilities for causal emergence. Used to explore and compute various information theory metrics for networks, such as effective information, effectiveness and causal emergence.

This package provides several confidence interval and testing procedures using event-specific win ratios for semi-competing risks data with non-terminal and terminal events, as developed in Yang et al. (2021<doi:10.1002/sim.9266>). Compared with conventional methods for survival data, these procedures are designed to utilize more data for improved inference procedures with semi-competing risks data. The event-specific win ratios were introduced in Yang and Troendle (2021<doi:10.1177/1740774520972408>). In this package, the event-specific win ratios and confidence intervals are obtained for each event type, and several testing procedures are developed for the global null of no treatment effect on either terminal or non-terminal events. Furthermore, a test of proportional hazard assumptions, under which the event-specific win ratios converge to the hazard ratios, and a test of equal hazard ratios are provided. For summarizing the treatment effect on all events, confidence intervals for linear combinations of the event-specific win ratios are available using pre-determined or data-driven weights. Asymptotic properties of these inference procedures are discussed in Yang et al (2021<doi:10.1002/sim.9266>). Also, transformations are used to yield better control of the type one error rates for moderately sized data sets.

Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community ecological data. The original package description is in Goslee and Urban (2007) <doi:10.18637/jss.v022.i07>, with further statistical detail in Goslee (2010) <doi:10.1007/s11258-009-9641-0>.

This is the course package for the exercise portion of the "Ecological Data Collection and Processing" course.

This package provides functions for converting objects to scalars (vectors of length 1) and a more inclusive definition of data that can be interpreted as numbers (numeric and complex alike).

Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for (i) plotting EEG data, (ii) filtering EEG data, (iii) smoothing EEG data; (iv) frequency domain (Fourier) analysis of EEG data, (v) Independent Component Analysis of EEG data, and (vi) simulating event-related potential EEG data.

Easily import multi-frequency acoustic data stored in HAC files (see <doi:10.17895/ices.pub.5482> for more information on the format), and produce echogram visualisations with predefined or customized color palettes. It is also possible to merge consecutive echograms; mask or delete unwanted echogram areas; model and subtract background noise; and more important, develop, test and interpret different combinations of frequencies in order to perform acoustic filtering of the echogram's data.

Use R to interface with the ETRADE API <https://developer.etrade.com/home>. Functions include authentication, trading, quote requests, account information, and option chains. A user will need an ETRADE brokerage account and ETRADE API approval. See README for authentication process and examples.

This package provides functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014) <DOI:10.1109/TKDE.2012.50>, <DOI:10.1016/j.ejor.2014.04.001>.

Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) <DOI: 10.1002/cem.2997>) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) <DOI: 10.1016/j.apr.2017.01.004>) and the third method (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>).

An implementation of the algorithm described in "Efficient Large- Scale Internet Media Selection Optimization for Online Display Advertising" by Paulson, Luo, and James (Journal of Marketing Research 2018; see URL below for journal text/citation and <http://faculty.marshall.usc.edu/gareth-james/Research/ELMSO.pdf> for a full-text version of the paper). The algorithm here is designed to allocate budget across a set of online advertising opportunities using a coordinate-descent approach, but it can be used in any resource-allocation problem with a matrix of visitation (in the case of the paper, website page- views) and channels (in the paper, websites). The package contains allocation functions both in the presence of bidding, when allocation is dependent on channel-specific cost curves, and when advertising costs are fixed at each channel.

Computation of direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items (Battauz (2013) <doi:10.1007/s11336-012-9316-y>, Battauz (2015) <doi:10.18637/jss.v068.i07>). Test scoring can be performed by true score equating and observed score equating methods. DIF detection can be performed using a Wald-type test (Battauz (2019) <doi:10.1007/s10260-018-00442-w>). The package includes tests to assess the stability of the equating transformations (Battauz(2022) <doi:10.1111/stan.12277>).

This package implements methods for functional data analysis based on the epigraph and hypograph indices. These methods transform functional datasets, whether in one or multiple dimensions, into multivariate datasets. The transformation involves applying the epigraph, hypograph, and their modified versions to both the original curves and their first and second derivatives. The calculation of these indices is tailored to the dimensionality of the functional dataset, with special considerations for dependencies between dimensions in multidimensional cases. This approach extends traditional multivariate data analysis techniques to the functional data setting. A key application of this package is the EHyClus method, which enhances clustering analysis for functional data across one or multiple dimensions using the epigraph and hypograph indices. See Pulido et al. (2023) <doi:10.1007/s11222-023-10213-7> and Pulido et al. (2024) <doi:10.48550/arXiv.2307.16720>.

Facilitates access to sample datasets from the EunomiaDatasets repository (<https://github.com/ohdsi/EunomiaDatasets>).

Calculates several indices, such as of diversity, fluctuation, etc., and they are used to estimate ecological indicators.

Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Fox M.P., MacLehose R.F., and Lash T.L. "Applying Quantitative Bias Analysis to Epidemiologic Data, second ed.", ('Springer', 2021).

This package provides several functions to simplify using the glmnet package: converting data frames into matrices ready for glmnet'; b) imputing missing variables multiple times; c) fitting and applying prediction models straightforwardly; d) assigning observations to folds in a balanced way; e) cross-validate the models; f) selecting the most representative model across imputations and folds; and g) getting the relevance of the model regressors; as described in several publications: Solanes et al. (2022) <doi:10.1038/s41537-022-00309-w>, Palau et al. (2023) <doi:10.1016/j.rpsm.2023.01.001>, Sobregrau et al. (2024) <doi:10.1016/j.jpsychores.2024.111656>.

Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. The evtree package implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while the partykit package is leveraged to represent the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions.