This package contains implementations of the integrative Cox model with uncertain event times proposed by Wang, et al. (2020) <doi:10.1214/19-AOAS1287>, the regularized Cox cure rate model with uncertain event status proposed by Wang, et al. (2023) <doi:10.1007/s12561-023-09374-w>, and other survival analysis routines including the Cox cure rate model proposed by Kuk and Chen (1992) <doi:10.1093/biomet/79.3.531> via an EM algorithm proposed by Sy and Taylor (2000) <doi:10.1111/j.0006-341X.2000.00227.x>, the regularized Cox cure rate model with elastic net penalty following Masud et al. (2018) <doi:10.1177/0962280216677748>.

The Integro-Difference Equation model is a linear, dynamical model used to model phenomena that evolve in space and in time; see, for example, Cressie and Wikle (2011, ISBN:978-0-471-69274-4) or Dewar et al. (2009) <doi:10.1109/TSP.2008.2005091>. At the heart of the model is the kernel, which dictates how the process evolves from one time point to the next. Both process and parameter reduction are used to facilitate computation, and spatially-varying kernels are allowed. Data used to estimate the parameters are assumed to be readings of the process corrupted by Gaussian measurement error. Parameters are fitted by maximum likelihood, and estimation is carried out using an evolution algorithm.

Infix operators to detect, subset, and replace the elements matched by a given condition. The functions have several variants of operator types, including subsets, ranges, regular expressions and others. Implemented operators work on vectors, matrices, and lists.

Simulate general insurance policies, losses and loss emergence. The functions contemplate deterministic and stochastic policy retention and growth scenarios. Retention and growth rates are percentages relative to the expiring portfolio. Claims are simulated for each policy. This is accomplished either be assuming a frequency distribution per development lag or by generating random wait times until claim emergence and settlement. Loss simulation uses standard loss distributions for claim amounts.

This package provides a function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>. This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes.

This package provides a toolkit for causal inference in experimental and observational studies. Implements various simple Bayesian models including linear, negative binomial, and logistic regression for impact estimation. Provides functionality for randomization and checking baseline equivalence in experimental designs. The package aims to simplify the process of impact measurement for researchers and analysts across different fields. Examples and detailed usage instructions are available at <https://book.martinez.fyi>.

Computes individual contributions to the overall Gini and Theil's T and Theil's L measures and their decompositions by groups such as race, gender, national origin, with the three functions of iGini(), iTheiT(), and iTheilL(). For details, see Tim F. Liao (2019) <doi:10.1177/0049124119875961>.

IRT-M is a semi-supervised approach based on Bayesian Item Response Theory that produces theoretically identified underlying dimensions from input data and a constraints matrix. The methodology is fully described in Morucci et al. (2024), "Measurement That Matches Theory: Theory-Driven Identification in Item Response Theory Models"'. Details are available at <https://www.cambridge.org/core/journals/american-political-science-review/article/measurement-that-matches-theory-theorydriven-identification-in-item-response-theory-models/395DA1DFE3DCD7B866DC053D7554A30B>.

This package provides native R access to Interactive Brokers Trader Workstation API.

This package provides a set of tools for evaluating several measures of case influence for structural equation models.

Convert between Irish grid references and Irish Grid coordinates. Irish grid references can also be converted to or from an sf object in any coordinate reference system. Precisions from 1 m to 100 km including 2 km (tetrads) are supported, as are datasets with mixed precision. Conversion to sf polygons is precision-aware.

This package provides functions for analyzing multiple choice items. These analyses include the convertion of student response into binaty data (correct/incorrect), the computation of the number of corrected responses and grade for each subject, the calculation of item difficulty and discrimination, the computation of the frecuency and point-biserial correlation for each distractor and the graphical analysis of each item.

This package provides a fragmentation spectra detection pipeline for high-throughput LC/HRMS data processing using peaklists generated by the IDSL.IPA workflow <doi:10.1021/acs.jproteome.2c00120>. The IDSL.CSA package can deconvolute fragmentation spectra from Composite Spectra Analysis (CSA), Data Dependent Acquisition (DDA) analysis, and various Data-Independent Acquisition (DIA) methods such as MS^E, All-Ion Fragmentation (AIF) and SWATH-MS analysis. The IDSL.CSA package was introduced in <doi:10.1021/acs.analchem.3c00376>.

An implementation of various methods for estimating intrinsic dimension of vector-valued dataset or distance matrix. Most methods implemented are based on different notion of fractal dimension such as the capacity dimension, the box-counting dimension, and the information dimension.

Three methods for Individual Tree Crowns (ITCs) delineation on remote sensing data: one is based on LiDAR data in x,y,z format and one on imagery data in raster format.

This package implements the standard D-Scoring algorithm (Greenwald, Banaji, & Nosek, 2003) for Implicit Association Test (IAT) data and includes plotting capabilities for exploring raw IAT data.

An implementation of randomization-based hypothesis testing for three different estimands in a cluster-randomized encouragement experiment. The three estimands include (1) testing a cluster-level constant proportional treatment effect (Fisher's sharp null hypothesis), (2) pooled effect ratio, and (3) average cluster effect ratio. To test the third estimand, user needs to install Gurobi (>= 9.0.1) optimizer via its R API. Please refer to <https://www.gurobi.com/documentation/9.0/refman/ins_the_r_package.html>.

Interfaces for choosing important predictors in supervised regression, classification, and censored regression models. Permuted importance scores (Biecek and Burzykowski (2021) <doi:10.1201/9780429027192>) can be computed for tidymodels model fits.

An implementation to reconstruct individual patient data from Kaplan-Meier (K-M) survival curves, visualize and assess the accuracy of the reconstruction, then perform secondary analysis on the reconstructed data. We involve a simple function to extract the coordinates form the published K-M curves. The function is developed based on Poisot T. â s digitize package (2011) <doi:10.32614/RJ-2011-004> . For more complex and tangled together graphs, digitizing software, such as DigitizeIt (for MAC or windows) or ScanIt'(for windows) can be used to get the coordinates. Additional information should also be involved to increase the accuracy, like numbers of patients at risk (often reported at 5-10 time points under the x-axis of the K-M graph), total number of patients, and total number of events. The package implements the modified iterative K-M estimation algorithm (modified-iKM) improved upon the approach proposed by Guyot (2012) <doi:10.1186/1471-2288-12-9> with some modifications.

The IDSL.FSA package was designed to annotate standard .msp (mass spectra format) and .mgf (Mascot generic format) files using mass spectral entropy similarity, dot product (cosine) similarity, and normalized Euclidean mass error (NEME) followed by intelligent pre-filtering steps for rapid spectra searches. IDSL.FSA also provides a number of modules to convert and manipulate .msp and .mgf files. The IDSL.FSA workflow was integrated in the IDSL.CSA and IDSL.NPA packages introduced in <doi:10.1021/acs.analchem.3c00376>.

Companion package to the book "industRial data science", J.Ramalho (2021) <https://j-ramalho.github.io/industRial/>. Provides data sets and functions to complete the case studies and contains the book original Rmd files and tutorials.

This package provides functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.

Method for the calculation of copy numbers and calling of copy number alterations. The algorithm uses coverage data from amplicon sequencing of a sample cohort as input. The method includes significance assessment, correction for multiple testing and does not depend on normal DNA controls. Budczies (2016 Mar 15) <doi:10.18632/oncotarget.7451>.

This package provides a collection of several functions related to construction and analysis of incomplete split-plot designs. The package contains functions to obtain and analyze incomplete split-plot designs for three kinds of situations namely (i) when blocks are complete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments, (ii) when blocks are incomplete with respect to main plot treatments and main plots are complete with respect to subplot treatments and (iii) when blocks are incomplete with respect to main plot treatments and main plots are incomplete with respect to subplot treatments.