Handy functions and data to support the course book Empirical Research in Accounting: Tools and Methods (1st ed.). Chapman and Hall/CRC. <doi:10.1201/9781003456230> and <https://iangow.github.io/far_book/>.

Kiener distributions K1, K2, K3, K4 and K7 to characterize distributions with left and right, symmetric or asymmetric fat tails in finance, neuroscience and other disciplines. Two algorithms to estimate the distribution parameters, quantiles, value-at-risk and expected shortfall. IMPORTANT: Standardization has been changed in versions >= 2.0.0 to get sd = 1 when kappa = Inf rather than 2*pi/sqrt(3) in versions <= 1.8.6. This affects parameter g (other parameters stay unchanged). Do not update if you need consistent comparisons with previous results for the g parameter.

Useful tools for conveniently downloading FHIR resources in xml format and converting them to R data.frames. The package uses FHIR-search to download bundles from a FHIR server, provides functions to save and read xml-files containing such bundles and allows flattening the bundles to data.frames using XPath expressions. FHIR® is the registered trademark of HL7 and is used with the permission of HL7. Use of the FHIR trademark does not constitute endorsement of this product by HL7.

Computes different multidimensional FD indices. Implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. Also contains other useful tools for functional ecology.

Estimates the sample size for a test or a trial based on repeated simulation using a model based approach. Implements a method by Maruo et al. (2018) <doi:10.1080/19466315.2017.1349689> and an extension.

An implementation of sparsity-ranked lasso and related methods for time series data. This methodology is especially useful for large time series with exogenous features and/or complex seasonality. Originally described in Peterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7> in the context of variable selection with interactions and/or polynomials, ranked sparsity is a philosophy with methods useful for variable selection in the presence of prior informational asymmetry. This situation exists for time series data with complex seasonality, as shown in Peterson and Cavanaugh (2024) <doi:10.1177/1471082X231225307>, which also describes this package in greater detail. The sparsity-ranked penalization methods for time series implemented in fastTS can fit large/complex/high-frequency time series quickly, even with a high-dimensional exogenous feature set. The method is considerably faster than its competitors, while often producing more accurate predictions. Also included is a long hourly series of arrivals into the University of Iowa Emergency Department with concurrent local temperature.

Computes functional rarity indices as proposed by Violle et al. (2017) <doi:10.1016/j.tree.2017.02.002>. Various indices can be computed using both regional and local information. Functional Rarity combines both the functional aspect of rarity as well as the extent aspect of rarity. funrar is presented in Grenié et al. (2017) <doi:10.1111/ddi.12629>.

With the functions in this package you can check the validity of the following financial instrument identifiers: FIGI (Financial Instrument Global Identifier <https://www.openfigi.com/about/figi>), CUSIP (Committee on Uniform Security Identification Procedures <https://www.cusip.com/identifiers.html#/CUSIP>), ISIN (International Securities Identification Number <https://www.cusip.com/identifiers.html#/ISIN>), SEDOL (Stock Exchange Daily Official List <https://www2.lseg.com/SEDOL-masterfile-service-tech-guide-v8.6>). You can also calculate the FIGI checksum of 11-character strings, which can be useful if you want to create your own FIGI identifiers.

This package contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".

Simple key-value database using SQLite as the backend.

This package provides functions and example datasets for Fechnerian scaling of discrete object sets. User can compute Fechnerian distances among objects representing subjective dissimilarities, and other related information. See package?fechner for an overview.

Simplifies the creation and customization of forest plots (alternatively called dot-and-whisker plots). Input classes accepted by forplo are data.frame, matrix, lm, glm, and coxph. forplo was written in base R and does not depend on other packages.

Two Gray Level Co-occurrence Matrix ('GLCM') implementations are included: The first is a fast GLCM feature texture computation based on Python Numpy arrays ('Github Repository, <https://github.com/tzm030329/GLCM>). The second is a fast GLCM RcppArmadillo implementation which is parallelized (using OpenMP') with the option to return all GLCM features at once. For more information, see "Artifact-Free Thin Cloud Removal Using Gans" by Toizumi Takahiro, Zini Simone, Sagi Kazutoshi, Kaneko Eiji, Tsukada Masato, Schettini Raimondo (2019), IEEE International Conference on Image Processing (ICIP), pp. 3596-3600, <doi:10.1109/ICIP.2019.8803652>.

This package provides algorithms to fit linear regression models under several popular penalization techniques and functional linear regression models based on Majorizing-Minimizing (MM) and Alternating Direction Method of Multipliers (ADMM) techniques. See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.

Calculation of AHP (Analytic Hierarchy Process - <http://en.wikipedia.org/wiki/Analytic_hierarchy_process>) with classic and fuzzy weights based on Saaty's pairwise comparison method for determination of weights.

Create an interactive function map by analyzing a specified R script. It uses the find_dependencies() function from the functiondepends package to recursively trace all user-defined function dependencies.

Simulates plot data in multi-environment field trials with one or more traits. Its core function generates plot errors that capture spatial trend, random error (noise), and extraneous variation, which are combined at a user-defined ratio. Phenotypes can be generated by combining the plot errors with simulated genetic values that capture genotype-by-environment (GxE) interaction using wrapper functions for the R package `AlphaSimR`.

Create Frequently Asked Questions page for Shiny application.

This package implements the algorithm by Briefs and Bläser (2025) <https://openreview.net/forum?id=8PHOPPH35D>, based on the approach of Gupta and Bläser (2024) <doi:10.1609/aaai.v38i18.30023>. It determines, for a structural causal model (SCM) whose directed edges form a tree, whether each parameter is unidentifiable, 1-identifiable or 2-identifiable (other cases cannot occur), using a randomized algorithm with provable running time O(n^3 log^2 n).

The main functions in this package are with_cache() and cached_read(). The former is a simple way to cache an R object into a file on disk, using cachem'. The latter is a wrapper around any standard read function, but caches both the output and the file list info. If the input file list info hasn't changed, the cache is used; otherwise, the original files are re-read. This can save time if the original operation requires reading from many files, and/or involves lots of processing.

Estimates and provides inference for quantities that assess high dimensional mediation and potential surrogate markers including the direct effect of treatment, indirect effect of treatment, and the proportion of treatment effect explained by a surrogate/mediator; details are described in Zhou et al (2022) <doi:10.1002/sim.9352> and Zhou et al (2020) <doi:10.1093/biomet/asaa016>. This package relies on the optimization software MOSEK', <https://www.mosek.com>.

Estimates heterogeneous effects in factorial (and conjoint) models. The methodology employs a Bayesian finite mixture of regularized logistic regressions, where moderators can affect each observation's probability of group membership and a sparsity-inducing prior fuses together levels of each factor while respecting ANOVA-style sum-to-zero constraints. Goplerud, Imai, and Pashley (2024) <doi:10.48550/ARXIV.2201.01357> provide further details.

This package provides design-based and model-based estimators for the population average marginal component effects in general factorial experiments, including conjoint analysis. The package also implements a series of recommendations offered in de la Cuesta, Egami, and Imai (2022) <doi:10.1017/pan.2020.40>, and Egami and Imai (2019) <doi:10.1080/01621459.2018.1476246>.

This package implements methods for multiple change point detection in multivariate time series with non-stationary dynamics and cross-correlations. The methodology is based on a model in which each component has a fluctuating mean represented by a random walk with occasional abrupt shifts, combined with a stationary vector autoregressive structure to capture temporal and cross-sectional dependence. The framework is broadly applicable to correlated multivariate sequences in which large, sudden shifts occur in all or subsets of components and are the primary targets of interest, whereas small, smooth fluctuations are not. Although random walks are used as a modeling device, they provide a flexible approximation for a wide class of slowly varying or locally smooth dynamics, enabling robust performance beyond the strict random walk setting.