Adds menu items for case 2 (profile case) best-worst scaling (BWS2) to the R Commander. BWS2 is a question-based survey method that constructs profiles (combinations of attribute levels) using an orthogonal array, asks respondents to select the best and worst levels in each profile, and measures preferences for attribute levels by analyzing the responses. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.

STG is a method for feature selection in neural network. The procedure is based on probabilistic relaxation of the l0 norm of features, or the count of the number of selected features. The framework simultaneously learns either a nonlinear regression or classification function while selecting a small subset of features. Read more: Yamada et al. (2020) <https://proceedings.mlr.press/v119/yamada20a.html>.

For the calculation of sample size or power in a two-group repeated measures design, accounting for attrition and accommodating a variety of correlation structures for the repeated measures; details of the method can be found in the scientific paper: Donald Hedeker, Robert D. Gibbons, Christine Waternaux (1999) <doi:10.3102/10769986024001070>.

The package contains all the data sets related to the book written by the maintainer of the package.

Computes 26 financial risk measures for any continuous distribution. The 26 financial risk measures include value at risk, expected shortfall due to Artzner et al. (1999) <DOI:10.1007/s10957-011-9968-2>, tail conditional median due to Kou et al. (2013) <DOI:10.1287/moor.1120.0577>, expectiles due to Newey and Powell (1987) <DOI:10.2307/1911031>, beyond value at risk due to Longin (2001) <DOI:10.3905/jod.2001.319161>, expected proportional shortfall due to Belzunce et al. (2012) <DOI:10.1016/j.insmatheco.2012.05.003>, elementary risk measure due to Ahmadi-Javid (2012) <DOI:10.1007/s10957-011-9968-2>, omega due to Shadwick and Keating (2002), sortino ratio due to Rollinger and Hoffman (2013), kappa due to Kaplan and Knowles (2004), Wang (1998)'s <DOI:10.1080/10920277.1998.10595708> risk measures, Stone (1973)'s <DOI:10.2307/2978638> risk measures, Luce (1980)'s <DOI:10.1007/BF00135033> risk measures, Sarin (1987)'s <DOI:10.1007/BF00126387> risk measures, Bronshtein and Kurelenkova (2009)'s risk measures.

This package provides a collection of randomization tests, data sets and examples. The current version focuses on five testing problems and their implementation in empirical work. First, it facilitates the empirical researcher to test for particular hypotheses, such as comparisons of means, medians, and variances from k populations using robust permutation tests, which asymptotic validity holds under very weak assumptions, while retaining the exact rejection probability in finite samples when the underlying distributions are identical. Second, the description and implementation of a permutation test for testing the continuity assumption of the baseline covariates in the sharp regression discontinuity design (RDD) as in Canay and Kamat (2018) <https://goo.gl/UZFqt7>. More specifically, it allows the user to select a set of covariates and test the aforementioned hypothesis using a permutation test based on the Cramer-von Misses test statistic. Graphical inspection of the empirical CDF and histograms for the variables of interest is also supported in the package. Third, it provides the practitioner with an effortless implementation of a permutation test based on the martingale decomposition of the empirical process for testing for heterogeneous treatment effects in the presence of an estimated nuisance parameter as in Chung and Olivares (2021) <doi:10.1016/j.jeconom.2020.09.015>. Fourth, this version considers the two-sample goodness-of-fit testing problem under covariate adaptive randomization and implements a permutation test based on a prepivoted Kolmogorov-Smirnov test statistic. Lastly, it implements an asymptotically valid permutation test based on the quantile process for the hypothesis of constant quantile treatment effects in the presence of an estimated nuisance parameter.

These tools help you to assess if a corporate lending portfolio aligns with climate goals. They summarize key climate indicators attributed to the portfolio (e.g. production, emission factors), and calculate alignment targets based on climate scenarios. They implement in R the last step of the free software PACTA (Paris Agreement Capital Transition Assessment; <https://www.transitionmonitor.com/>). Financial institutions use PACTA to study how their capital allocation decisions align with climate change mitigation goals.

The rearrangement operator (Hardy, Littlewood, and Polya 1952) for univariate, bivariate, and trivariate point estimates of monotonic functions. The package additionally provides a function that creates simultaneous confidence intervals for univariate functions and applies the rearrangement operator to these confidence intervals.

Offers bathymetric interpolation using Inverse Distance Weighted and Ordinary Kriging via the gstat and terra packages. Other functions focus on quantifying physical aquatic habitats (e.g., littoral, epliminion, metalimnion, hypolimnion) from interpolated digital elevation models (DEMs). Functions were designed to calculate these metrics across water levels for use in reservoirs but can be applied to any DEM and will provide values for fixed conditions. Parameters like Secchi disk depth or estimated photic zone, thermocline depth, and water level fluctuation depth are included in most functions.

BEAST is a Bayesian estimator of abrupt change, seasonality, and trend for decomposing univariate time series and 1D sequential data. Interpretation of time series depends on model choice; different models can yield contrasting or contradicting estimates of patterns, trends, and mechanisms. BEAST alleviates this by abandoning the single-best-model paradigm and instead using Bayesian model averaging over many competing decompositions. It detects and characterizes abrupt changes (changepoints, breakpoints, structural breaks, joinpoints), cyclic or seasonal variation, and nonlinear trends. BEAST not only detects when changes occur but also quantifies how likely the changes are true. It estimates not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is generically applicable to any real-valued time series, such as those from remote sensing, economics, climate science, ecology, hydrology, and other environmental and biological systems. Example applications include identifying regime shifts in ecological data, mapping forest disturbance and land degradation from satellite image time series, detecting market trends in economic indicators, pinpointing anomalies and extreme events in climate records, and analyzing system dynamics in biological time series. Details are given in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.

This package provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc).

This package provides a tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process.

This package performs comparative bioavailability calculations for Average Bioequivalence with Expanding Limits (ABEL). Implemented are Method A / Method B and the detection of outliers. If the design allows, assessment of the empiric Type I Error and iteratively adjusting alpha to control the consumer risk. Average Bioequivalence - optionally with a tighter (narrow therapeutic index drugs) or wider acceptance range (South Africa: Cmax) - is implemented as well.

Reallocating the respective lessons by hours (respecting the constraints induced by the existence of coupled lessons) so that the total number of gaps is as small as possible.

This package provides a series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.

Assessing the comparative performance of two logistic regression models or results of such models or classification models. Discrimination metrics include Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), and difference in Area Under the Curves (AUCs), Brier scores and Brier skill. Plots include Risk Assessment Plots, Decision curves and Calibration plots. Methods are described in Pickering and Endre (2012) <doi:10.1373/clinchem.2011.167965> and Pencina et al. (2008) <doi:10.1002/sim.2929>.

This package performs univariate probability mass function estimation via Bayesian nonparametric mixtures of rounded kernels as in Canale and Dunson (2011) <doi:10.1198/jasa.2011.tm10552>.

This package contains all the code examples in the book "R for Dummies" (2nd edition) by Andrie de Vries and Joris Meys. You can view the table of contents as well as the sample code for each chapter.

Build regular expressions using grammar and functionality inspired by <https://github.com/VerbalExpressions>. Usage of the %>% is encouraged to build expressions in a chain-like fashion.

R bindings for functions from the UCR Suite by Rakthanmanon et al. (2012) <DOI:10.1145/2339530.2339576>, which enables ultrafast subsequence search for a best match under Dynamic Time Warping and Euclidean Distance.

Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.

Parameters estimation and linear regression models for Reliability distributions families reviewed by Almalki & Nadarajah (2014) <doi:10.1016/j.ress.2013.11.010> using Generalized Additive Models for Location, Scale and Shape, aka GAMLSS by Rigby & Stasinopoulos (2005) <doi:10.1111/j.1467-9876.2005.00510.x>.

Robust categorical data analysis based on the theory of C-estimation developed in Welz (2024) <doi:10.48550/arXiv.2403.11954>. For now, the package only implements robust estimation of polychoric correlation as proposed in Welz, Mair and Alfons (2024) <doi:10.48550/arXiv.2407.18835> with methods for printing and plotting. We will implement further models in future releases. In addition, the package is still experimental, so input arguments and class structure may change in future releases.

An interface to iDigBio's search API that allows downloading specimen records. Searches are returned as a data.frame. Other functions such as the metadata end points return lists of information. iDigBio is a US project focused on digitizing and serving museum specimen collections on the web. See <https://www.idigbio.org> for information on iDigBio.