Single-Index Models with a Surface-Link

An implementation of a single-index regression for optimizing individualized dose rules from an observational study. To model interaction effects between baseline covariates and a treatment variable defined on a continuum, we employ two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear combination of the covariates (a single-index). An unspecified main effect for the covariates is allowed, which can also be modeled through a parametric model. A unique contribution of this work is in the parsimonious single-index parametrization specifically defined for the interaction effect term. We refer to Park, Petkova, Tarpey, and Ogden (2020) (for the case of a discrete treatment) and Park, Petkova, Tarpey, and Ogden (2021) "A single-index model with a surface-link for optimizing individualized dose rules" for detail of the method. The model can take a member of the exponential family as a response variable and can also take an ordinal categorical response. The main function of this package is simsl().

Compute Uniform Error Index

Uniform Error Index is the weighted average of different error measures. Uniform Error Index utilizes output from different error function and gives more robust and stable error values. This package has been developed to compute Uniform Error Index from ten different loss function like Error Square, Square of Square Error, Quasi Likelihood Error, LogR-Square, Absolute Error, Absolute Square Error etc. The weights are determined using Principal Component Analysis (PCA) algorithm of Yeasin and Paul (2024) .

Penalized Youden Index Estimator

Implements the Penalized Youden Index Estimator (PYE) and the Covariate-Adjusted Youden Index Estimator (covYI), providing a novel framework for feature and covariate selection and combination in high-dimensional binary classification problems. Methodologies are based on Salaroli and Pardo (2023) and an unpublished manuscript by Salaroli and Pardo (2026) under review.

Pacific Decadal Oscillation Index Data

Monthly Pacific Decadal Oscillation (PDO) index values from January 1900 to September 2018. Superseded by 'rsoi' package which includes the historical and most recent monthly PDO index values together with related climate indices.

Constrained Groupwise Additive Index Models

Fits constrained groupwise additive index models and provides functions for inference and interpretation of these models. The method is described in Masselot, Chebana, Campagna, Lavigne, Ouarda, Gosselin (2022) "Constrained groupwise additive index models" .

Calculates the Wavelet-Based Organization Index

Calculates the wavelet-based organization index following Brune et al (2018) (), the modified wavelet-based organization index and the local wavelet-based organization index of an arbitrary 2D array using Wavelet Transforms of the LS2W package by Eckley et al (2010) () and Eckley and Nason (2011) (). In Version 1.0.3 the calculation of LW is added.

Duration Curve Hydrological Model Indexes

Compute duration curves of daily flow series, both real and modeled, to be compared through indexes of flow duration curves. The package functions include comparative plots and goodness of fit tests. Flow duration curve indexes are based on: Yilmaz et al., (2008) .

An R Package for Single-Index Quantile Regression

Single-Index Quantile Regression is effective in some scenarios. We provides functions that allow users to fit Single-Index Quantile Regression model. It also provides functions to do prediction, estimate standard errors of the single-index coefficients via bootstrap, and visualize the estimated univariate function. Please see W., Y., Y. (2010) for details.

Preference Selection Index Method (PSIM)

The Preference Selection Index Method was created in (2010) and provides an innovative approach to determining the relative importance of criteria without pairwise comparisons, unlike the Analytic Hierarchy Process. The Preference Selection Index Method uses statistical methods to calculate the criteria weights and reflects their relative importance in the final decision-making process, offering an objective and non-subjective solution. This method is beneficial in multi-criteria decision analysis. The 'PSIM' package provides a practical and accessible tool for implementing the Preference Selection Index Method in R. It calculates the weights of criteria and makes the method available to researchers, analysts, and professionals without the need to develop complex calculations manually. More details about the Preference Selection Index Method can be found in Maniya K. and Bhatt M. G.(2010) .

Divisia Monetary Aggregates Index

Functions to calculate Divisia monetary aggregates index as given in Barnett, W. A. (1980) ().