Automated compound deconvolution, alignment across samples, and identification of metabolites by spectral library matching in Gas Chromatography - Mass spectrometry (GC-MS) untargeted metabolomics. Outputs a table with compound names, matching scores and the integrated area of the compound for each sample. Package implementation is described in Domingo-Almenara et al. (2016) <doi:10.1021/acs.analchem.6b02927>.

Estimates unknown historical or archaeological dates subject to relationships with other dates and absolute constraints, derived as marginal densities from the full joint conditional distribution. Includes rule-based estimation of the production dates of artifact types. Collins-Elliott (2024) <https://volweb.utk.edu/~scolli46/sceGUTChronology.pdf>.

Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression, proposed by Yang, Y., Qian, W. and Zou, H. (2018) <doi:10.1080/00949655.2013.876024>. The code is based on the 'gbm' package originally developed by Greg Ridgeway.

Provides a methodology simple and trustworthy for the analysis of extreme values and multiple threshold tests for a generalized Pareto distribution, together
    with an automatic threshold selection algorithm. See del Castillo, J, Daoudi, J and Lockhart, R (2014) <doi:10.1111/sjos.12037>.

Build entity relationship diagrams (ERD) to specify the nature of the relationship between tables in a database.

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.

Fits the Expectile Regression for Fixed Effect (ERFE)
    estimator. The ERFE model extends the within-transformation strategy
    to solve the incidental parameter problem within the expectile
    regression framework. The ERFE model estimates the regressor effects
    on the expectiles of the response distribution.  The ERFE estimate
    corresponds to the classical fixed-effect within-estimator when the
    asymmetric point is 0.5. The paper by Barry, Oualkacha, and
    Charpentier (2021, <arXiv:2108.04737>) gives more details about the
    ERFE model.