Runs a Shiny App in the local machine for basic statistical and graphical analyses. The point-and-click interface of Shiny App enables obtaining the same analysis outputs (e.g., plots and tables) more quickly, as compared with typing the required code in R, especially for users without much experience or expertise with coding. Examples of possible analyses include tabulating descriptive statistics for a variable, creating histograms by experimental groups, and creating a scatter plot and calculating the correlation between two variables.

Computing economic analysis in civil infrastructure and ecosystem restoration projects is a typical activity. This package contains Standard cost engineering and engineering economics methods that are applied to convert between present, future, and annualized costs. Newnan D. (2020) <ISBN 9780190931919> â Engineering Economic Analysisâ .

Detect events in time-series data. Combines multiple well-known R packages like forecast and neuralnet to deliver an easily configurable tool for multivariate event detection.

Estimation of fully and partially observed Exponential-Family Random Network Models (ERNM). Exponential-family Random Graph Models (ERGM) and Gibbs Fields are special cases of ERNMs and can also be estimated with the package. Please cite Fellows and Handcock (2012), "Exponential-family Random Network Models" available at <doi:10.48550/arXiv.1208.0121>.

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.

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.

Extends the Changes-in-Changes model a la Athey and Imbens (2006) <doi:10.1111/j.1468-0262.2006.00668.x> to multiple cohorts and time periods, which generalizes difference-in-differences estimation techniques to the entire distribution. Computes quantile treatment effects for every possible two-by-two combination in ecic(). Then, aggregating all bootstrap runs adds the standard errors in summary_ecic(). Results can be plotted with plot_ecic() aggregated over all cohort-group combinations or in an event-study style for either individual periods or individual quantiles.

The summation notation suggested by Einstein (1916) <doi:10.1002/andp.19163540702> is a concise mathematical notation that implicitly sums over repeated indices of n-dimensional arrays. Many ordinary matrix operations (e.g. transpose, matrix multiplication, scalar product, diag()', trace etc.) can be written using Einstein notation. The notation is particularly convenient for expressing operations on arrays with more than two dimensions because the respective operators ('tensor products') might not have a standardized name.

Analyses EuFMDiS output files in a Shiny App. The distributions of relevant output parameters are described in form of tables (quantiles) and plots. The App is called using eufmdis.adapt::run_adapt().

Estimating individual-level covariate-outcome associations using aggregate data ("ecological inference") or a combination of aggregate and individual-level data ("hierarchical related regression").

Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. EL calculations are based on J. Valeinis, E. Cers (2011) <http://home.lu.lv/~valeinis/lv/petnieciba/EL_TwoSample_2011.pdf>.

This package provides functions for the echelon analysis proposed by Myers et al. (1997) <doi:10.1023/A:1018518327329>, and the detection of spatial clusters using echelon scan method proposed by Kurihara (2003) <doi:10.20551/jscswabun.15.2_171>.

Implementation of the Centre of Gravity method and the Extrapolated Centre of Gravity method. It supports replicated observations. Cameron, D.G., et al (1982) <doi:10.1366/0003702824638610> JCGM (2008) <doi:10.59161/JCGM100-2008E>.

Simulating multi-arm cluster-randomized, multi-site, and simple randomized trials. Includes functions for conducting multilevel analyses using both Bayesian and Frequentist methods. Supports futility and superiority analyses through Bayesian approaches, along with visualization tools to aid interpretation and presentation of results.

Calculates marginal effects and conducts process analysis in exponential family random graph models (ERGM). Includes functions to conduct mediation and moderation analyses and to diagnose multicollinearity. URL: <https://github.com/sduxbury/ergMargins>. BugReports: <https://github.com/sduxbury/ergMargins/issues>. Duxbury, Scott W (2021) <doi:10.1177/0049124120986178>. Long, J. Scott, and Sarah Mustillo (2018) <doi:10.1177/0049124118799374>. Mize, Trenton D. (2019) <doi:10.15195/v6.a4>. Karlson, Kristian Bernt, Anders Holm, and Richard Breen (2012) <doi:10.1177/0081175012444861>. Duxbury, Scott W (2018) <doi:10.1177/0049124118782543>. Duxbury, Scott W, Jenna Wertsching (2023) <doi:10.1016/j.socnet.2023.02.003>. Huang, Peng, Carter Butts (2023) <doi:10.1016/j.socnet.2023.07.001>.

Pacote para análise de delineamentos experimentais (DIC, DBC e DQL), experimentos em esquema fatorial duplo (em DIC e DBC), experimentos em parcelas subdivididas (em DIC e DBC), experimentos em esquema fatorial duplo com um tratamento adicional (em DIC e DBC), experimentos em fatorial triplo (em DIC e DBC) e experimentos em esquema fatorial triplo com um tratamento adicional (em DIC e DBC), fazendo analise de variancia e comparacao de multiplas medias (para tratamentos qualitativos), ou ajustando modelos de regressao ate a terceira potencia (para tratamentos quantitativos); analise de residuos (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.

Several functions, datasets, and sample codes related to empirical research in economics are included. They cover the marginal effects for binary or ordered choice models, static and dynamic Almost Ideal Demand System (AIDS) models, and a typical event analysis in finance.

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.

The encompassing test is developed based on multi-step-ahead predictions of two nested models as in Pitarakis, J. (2023) <doi:10.48550/arXiv.2312.16099>. The statistics are standardised to a normal distribution, and the null hypothesis is that the larger model contains no additional useful information. P-values will be provided in the output.

This package provides a plotting package for climate science and services. Provides a set of functions for visualizing climate data, including maps, time series, scorecards and other diagnostics. Some functions are adapted and extended from the s2dv and CSTools packages (Manubens et al. (2018) <doi:10.1016/j.envsoft.2018.01.018>; Pérez-Zanón et al. (2022) <doi:10.5194/gmd-15-6115-2022>), with more consistent and integrated functionalities.

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.

This package provides tools to analyze the embryo growth and the sexualisation thermal reaction norms. See <doi:10.7717/peerj.8451> for tsd functions; see <doi:10.1016/j.jtherbio.2014.08.005> for thermal reaction norm of embryo growth.

Fast and very memory-efficient calculation of isotope patterns, subsequent convolution to theoretical envelopes (profiles) plus valley detection and centroidization or intensoid calculation. Batch processing, resolution interpolation, wrapper, adduct calculations and molecular formula parsing. Loos, M., Gerber, C., Corona, F., Hollender, J., Singer, H. (2015) <doi:10.1021/acs.analchem.5b00941>.

Package implements entropy balancing, a data preprocessing procedure described in Hainmueller (2008, <doi:10.1093/pan/mpr025>) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.