This package provides a system for calculating the optimal sampling effort, based on the ideas of "Ecological cost-benefit optimization" as developed by A. Underwood (1997, ISBN 0 521 55696 1). Data is obtained from simulated ecological communities with prep_data() which formats and arranges the initial data, and then the optimization follows the following procedure of four functions: (1) prep_data() takes the original dataset and creates simulated sets that can be used as a basis for estimating statistical power and type II error. (2) sim_beta() is used to estimate the statistical power for the different sampling efforts specified by the user. (3) sim_cbo() calculates then the optimal sampling effort, based on the statistical power and the sampling costs. Additionally, (4) scompvar() calculates the variation components necessary for (5) Underwood_cbo() to calculate the optimal combination of number of sites and samples depending on either an economic budget or on a desired statistical accuracy. Lastly, (6) plot_power() helps the user visualize the results of sim_beta().

This package provides EIOPA (European Insurance And Occupational Pensions Authority) risk-free rates. Please note that the author of this package is not affiliated with EIOPA. The data is accessed through a REST API available at <https://mehdiechchelh.com/api/>.

This package provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results.

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.

The cointegration based support vector regression model enables researchers to use data obtained from the cointegrating vector as input in the support vector regression model.

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 simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims. Full methods for EpiModel are detailed in Jenness et al. (2018, <doi:10.18637/jss.v084.i08>).

This package provides a principled framework for sampling Virtual Control Group (VCG) using energy distance-based covariate balancing. The package offers visualization tools to assess covariate balance and includes a permutation test to evaluate the statistical significance of observed deviations.

This package provides tools for accessing and analyzing eBird Status and Trends Data Products (<https://science.ebird.org/en/status-and-trends>). eBird (<https://ebird.org/home>) is a global database of bird observations collected by member of the public. eBird Status and Trends uses these data to model global bird distributions, abundances, and population trends at a high spatial and temporal resolution.

Import SPSS data, handle and change SPSS meta data, store and access large hierarchical data in SQLite data bases.

Method and tool for generating time series forecasts using an ensemble wavelet-based auto-regressive neural network architecture. This method provides additional support of exogenous variables and also generates confidence interval. This package provides EWNet model for time series forecasting based on the algorithm by Panja, et al. (2022) and Panja, et al. (2023) <arXiv:2206.10696> <doi:10.1016/j.chaos.2023.113124>.

Package for data exploration and result presentation. Full epicalc package with data management functions is available at <https://medipe.psu.ac.th/epicalc/>'.

An implementation for using efficient initials to compute the maximal eigenpair in R. It provides three algorithms to find the efficient initials under two cases: the tridiagonal matrix case and the general matrix case. Besides, it also provides two algorithms for the next to the maximal eigenpair under these two cases.

Replication methods to compute some basic statistic operations (means, standard deviations, frequency tables, percentiles, mean comparisons using weighted effect coding, generalized linear models, and linear multilevel models) in complex survey designs comprising multiple imputed or nested imputed variables and/or a clustered sampling structure which both deserve special procedures at least in estimating standard errors. See the package documentation for a more detailed description along with references.

Fit and plot some nonlinear models.

This package provides tools for modelling electric vehicle charging sessions into generic groups with similar connection patterns called "user profiles", using Gaussian Mixture Models clustering. The clustering and profiling methodology is described in Cañigueral and Meléndez (2021, ISBN:0142-0615) <doi:10.1016/j.ijepes.2021.107195>.

This package provides a light, simple tool for sending emails with minimal dependencies.

Access data related to the European union from GISCO <https://ec.europa.eu/eurostat/web/gisco>, the Geographic Information System of the European Commission, via its rest API at <https://gisco-services.ec.europa.eu>. This package tries to make it easier to get these data into R.

Evaluates the performance of binary classifiers. Computes confusion measures (TP, TN, FP, FN), derived measures (TPR, FDR, accuracy, F1, DOR, ..), and area under the curve. Outputs are well suited for nested dataframes.

Predicts enrollment and events at the design or analysis stage using specified enrollment and time-to-event models through simulations.

Simulates and estimates the Exponential Random Partition Model presented in the paper Hoffman, Block, and Snijders (2023) <doi:10.1177/00811750221145166>. It can also be used to estimate longitudinal partitions, following the model proposed in Hoffman and Chabot (2023) <doi:10.1016/j.socnet.2023.04.002>. The model is an exponential family distribution on the space of partitions (sets of non-overlapping groups) and is called in reference to the Exponential Random Graph Models (ERGM) for networks.

This package contains tools for formatting inline code, renaming redundant columns, aggregating age categories, adding survey weights, finding the earliest date of an event, plotting z-curves, generating population counts and formatting proportions with confidence intervals. This is part of the R4Epis project <https://r4epi.github.io/sitrep/>.

This package provides wrap functions to export and import graphics and data frames in R to microsoft office. And This package also provide write out figures with lots of different formats. Since people may work on the platform without GUI support, the package also provide function to easily write out figures to lots of different type of formats. Now this package provide function to extract colors from all types of figures and pdf files.

This package provides tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR.