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 assigning Clarke or Parkes (Consensus) error grid zones to blood glucose values, and for plotting both types of error grids in both mg/mL and mmol/L units.

Simulate ecological niche models using Mahalanobis distance, transform distances to suitability with 1 - empirical cumulative distribution function and 1 - chi-squared, and generate comparison figures.

Fit Bayesian (hierarchical) cognitive models using a linear modeling language interface using particle Metropolis Markov chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM), linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal race model (LNR) are supported. Additionally, users can specify their own likelihood function and/or choose for non-hierarchical estimation, as well as for a diagonal, blocked or full multivariate normal group-level distribution to test individual differences. Prior specification is facilitated through methods that visualize the (implied) prior. A wide range of plotting functions assist in assessing model convergence and posterior inference. Models can be easily evaluated using functions that plot posterior predictions or using relative model comparison metrics such as information criteria or Bayes factors. References: Stevenson et al. (2024) <doi:10.31234/osf.io/2e4dq>.

Automatic Generation of Exams in R for Sakai'. Question templates in the form of the exams package (see <https://www.r-exams.org/>) are transformed into XML format required by Sakai'.

This package provides set of functions aimed at epidemiologists. The package includes commands for measures of association and impact for case control studies and cohort studies. It may be particularly useful for outbreak investigations including univariable analysis and stratified analysis. The functions for cohort studies include the CS(), CSTable() and CSInter() commands. The functions for case control studies include the CC(), CCTable() and CCInter() commands. References - Cornfield, J. 1956. A statistical problem arising from retrospective studies. In Vol. 4 of Proceedings of the Third Berkeley Symposium, ed. J. Neyman, 135-148. Berkeley, CA - University of California Press. Woolf, B. 1955. On estimating the relation between blood group disease. Annals of Human Genetics 19 251-253. Reprinted in Evolution of Epidemiologic Ideas Annotated Readings on Concepts and Methods, ed. S. Greenland, pp. 108-110. Newton Lower Falls, MA Epidemiology Resources. Gilles Desve & Peter Makary, 2007. CSTABLE Stata module to calculate summary table for cohort study Statistical Software Components S456879, Boston College Department of Economics. Gilles Desve & Peter Makary, 2007. CCTABLE Stata module to calculate summary table for case-control study Statistical Software Components S456878, Boston College Department of Economics.

This package creates simple or stacked epidemic curves for hourly, daily, weekly or monthly outcome data.

Randomly generate a wide range of interaction networks with specified size, average degree, modularity, and topological structure. Sample nodes and links from within simulated networks randomly, by degree, by module, or by abundance. Simulations and sampling routines are implemented in FORTRAN', providing efficient generation times even for large networks. Basic visualization methods also included. Algorithms implemented here are described in de Aguiar et al. (2017) <arXiv:1708.01242>.

Easy and rapid quantitative estimation of small terrestrial ectotherm temperature regulation effectiveness in R. ectotemp is built on classical formulas that evaluate temperature regulation by means of various indices, inaugurated by Hertz et al. (1993) <doi: 10.1086/285573>. Options for bootstrapping and permutation testing are included to test hypotheses about divergence between organisms, species or populations.

Datasets from most recent CCIIO DIY entry in a tidy format. These support the Centers for Medicare and Medicaid Services (CMS) risk adjustment Do-It-Yourself (DIY) process, which allows health insurance issuers to calculate member risk profiles under the Health and Human Services-Hierarchical Condition Categories (HHS-HCC) regression model. This regression model is used to calculate risk adjustment transfers. Risk adjustment is a selection mitigation program implemented under the Patient Protection and Affordable Care Act (ACA or Obamacare) in the USA. Under the ACA, health insurance issuers submit claims data to CMS in order for CMS to calculate a risk score under the HHS-HCC regression model. However, CMS does not inform issuers of their average risk score until after the data submission deadline. These data sets can be used by issuers to calculate their average risk score mid-year. More information about risk adjustment and the HHS-HCC model can be found here: <https://www.cms.gov/mmrr/Articles/A2014/MMRR2014_004_03_a03.html>.

Create encrypted html files that are fully self contained and do not require any additional software. Using the package you can encrypt arbitrary html files and also directly create encrypted rmarkdown html reports.

Provide the EMU Speech Database Management System (EMU-SDMS) with database management, data extraction, data preparation and data visualization facilities. See <https://ips-lmu.github.io/The-EMU-SDMS-Manual/> for more details.

This package performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.

Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.

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.

We provide a non-parametric and a parametric approach to investigate the equivalence (or non-inferiority) of two survival curves, obtained from two given datasets. The test is based on the creation of confidence intervals at pre-specified time points. For the non-parametric approach, the curves are given by Kaplan-Meier curves and the variance for calculating the confidence intervals is obtained by Greenwood's formula. The parametric approach is based on estimating the underlying distribution, where the user can choose between a Weibull, Exponential, Gaussian, Logistic, Log-normal or a Log-logistic distribution. Estimates for the variance for calculating the confidence bands are obtained by a (parametric) bootstrap approach. For this bootstrap censoring is assumed to be exponentially distributed and estimates are obtained from the datasets under consideration. All details can be found in K.Moellenhoff and A.Tresch: Survival analysis under non-proportional hazards: investigating non-inferiority or equivalence in time-to-event data <arXiv:2009.06699>.

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.

The amplitude-dependent exponential autoregressive (EXPAR) time series model, initially proposed by Haggan and Ozaki (1981) <doi:10.2307/2335819> has been implemented in this package. Throughout various studies, the model has been found to adequately capture the cyclical nature of datasets. Parameter estimation of such family of models has been tackled by the approach of minimizing the residual sum of squares (RSS). Model selection among various candidate orders has been implemented using various information criteria, viz., Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). An illustration utilizing data of egg price indices has also been provided.

This package provides a set of functions for organising and analysing datasets from experiments run using Eyelink eye-trackers. Organising functions help to clean and prepare eye-tracking datasets for analysis, and mark up key events such as display changes and responses made by participants. Analysing functions help to create means for a wide range of standard measures (such as mean fixation durations'), which can then be fed into the appropriate statistical analyses and graphing packages as necessary.

Allows to calculate the probabilities of occurrences of an event in a great number of repetitions of Bernoulli experiment, through the application of the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. Gives the possibility to show the results graphically and analytically, and to compare the results obtained by the application of the above theorems with those calculated by the direct application of the Binomial formula. Is basically useful for educational purposes.

This package provides functions for extreme value theory, which may be divided into the following groups; exploratory data analysis, block maxima, peaks over thresholds (univariate and bivariate), point processes, gev/gpd distributions.

This package provides various statistical methods for designing and analyzing randomized experiments. One functionality of the package is the implementation of randomized-block and matched-pair designs based on possibly multivariate pre-treatment covariates. The package also provides the tools to analyze various randomized experiments including cluster randomized experiments, two-stage randomized experiments, randomized experiments with noncompliance, and randomized experiments with missing data.

Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial error (SEM), spatial Durbin (SDM), spatial error Durbin (SDEM) and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) <doi:10.1080/17421772.2022.2095426>.

This package provides a data transformation method which takes into account the special property of scale non-invariance with a breakpoint at 1 of the Euclidean distance.