This package provides a lasso-based method for building mechanistic models using the SAMBA algorithm (Stochastic Approximation for Model Building Algorithm) (M Prague, M Lavielle (2022) <doi:10.1002/psp4.12742>). The package extends the Rsmlx package (version 2024.1.0) to better handle high-dimensional data. It relies on the Monolix software (version 2024R1; see (<https://monolixsuite.slp-software.com/monolix/2024R1/>), which must be installed beforehand.

Automatically returns 24 logistic models including 13 individual models and 11 ensembles of models of logistic data. The package also returns 25 plots, 5 tables, and a summary report. The package automatically builds all 24 models, reports all results, and provides graphics to show how the models performed. This can be used for a wide range of data, such as sports or medical data. The package includes medical data (the Pima Indians data set), and information about the performance of Lebron James. The package can be used to analyze many other examples, such as stock market data. The package automatically returns many values for each model, such as True Positive Rate, True Negative Rate, False Positive Rate, False Negative Rate, Positive Predictive Value, Negative Predictive Value, F1 Score, Area Under the Curve. The package also returns 36 Receiver Operating Characteristic (ROC) curves for each of the 24 models.

Local explanations of machine learning models describe, how features contributed to a single prediction. This package implements an explanation method based on LIME (Local Interpretable Model-agnostic Explanations, see Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>) in which interpretable inputs are created based on local rather than global behaviour of each original feature.

Outlier detection using leave-one-out kernel density estimates and extreme value theory. The bandwidth for kernel density estimates is computed using persistent homology, a technique in topological data analysis. Using peak-over-threshold method, a generalized Pareto distribution is fitted to the log of leave-one-out kde values to identify outliers.

The leaflet JavaScript library provides many plugins some of which are available in the core leaflet package, but there are many more. It is not possible to support them all in the core leaflet package. This package serves as an add-on to the leaflet package by providing extra functionality via leaflet plugins.

This package provides a class that links matrix-like objects (nodes) by rows or by columns while behaving similarly to a base R matrix. Very large matrices are supported if the nodes are file-backed matrices.

The primary purpose of lavaan.mi is to extend the functionality of the R package lavaan', which implements structural equation modeling (SEM). When incomplete data have been multiply imputed, the imputed data sets can be analyzed by lavaan using complete-data estimation methods, but results must be pooled across imputations (Rubin, 1987, <doi:10.1002/9780470316696>). The lavaan.mi package automates the pooling of point and standard-error estimates, as well as a variety of test statistics, using a familiar interface that allows users to fit an SEM to multiple imputations as they would to a single data set using the lavaan package.

This package provides tools to decompose differences in cohort health expectancy (HE) by age and cause using longitudinal data. The package implements a novel longitudinal attribution method based on a semiparametric additive hazards model with time-dependent covariates, specifically designed to address interval censoring and semi-competing risks via a copula framework. The resulting age-cause-specific contributions to disability prevalence and death probability can be used to quantify and decompose differences in cohort HE between groups. The package supports stepwise replacement decomposition algorithms and is applicable to cohort-based health disparity research across diverse populations. Related methods include Sun et al. (2023) <doi:10.1177/09622802221133552>.

Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.

Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. Climatic drivers and geomorphometric characteristics were also compiled and are included for each lake. Data are part of the associated publication from the Global Lake Temperature Collaboration project (http://www.laketemperature.org). See citation('laketemps') for dataset attribution.

Fit response surfaces for datasets with latent-variable Gaussian process modeling, predict responses for new inputs, and plot latent variables locations in the latent space (only 1D or 2D). The input variables of the datasets can be quantitative, qualitative/categorical or mixed. The output variable of the datasets is a scalar (quantitative). The optimization of the likelihood function is done using a successive approximation/relaxation algorithm similar to another GP modeling package "GPM". The modeling method is published in "A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors" by Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (2018) <arXiv:1806.07504>. The package is developed in IDEAL of Northwestern University.

Gives users seeking federal lobbying disclosures an easier way to query the API maintained by the Senate federal lobbying disclosures database <https://lda.senate.gov/api/redoc/v1/> to find out how much companies and other entities are spending to lobby Congress and the federal government. It allows for search terms such as keywords, time periods and entity names. It then attempts to clean, or at least flag, filings that could provide incorrect results when seeking to answer the question: How much is being spent on lobbying our Congress and the administration and what issues do they care about?

Supervised learning techniques designed for the situation when the dimensionality exceeds the sample size have a tendency to overfit as the dimensionality of the data increases. To remedy this High dimensionality; low sample size (HDLSS) situation, we attempt to learn a lower-dimensional representation of the data before learning a classifier. That is, we project the data to a situation where the dimensionality is more manageable, and then are able to better apply standard classification or clustering techniques since we will have fewer dimensions to overfit. A number of previous works have focused on how to strategically reduce dimensionality in the unsupervised case, yet in the supervised HDLSS regime, few works have attempted to devise dimensionality reduction techniques that leverage the labels associated with the data. In this package and the associated manuscript Vogelstein et al. (2017) <arXiv:1709.01233>, we provide several methods for feature extraction, some utilizing labels and some not, along with easily extensible utilities to simplify cross-validative efforts to identify the best feature extraction method. Additionally, we include a series of adaptable benchmark simulations to serve as a standard for future investigative efforts into supervised HDLSS. Finally, we produce a comprehensive comparison of the included algorithms across a range of benchmark simulations and real data applications.

Simplex optimization algorithms as firstly proposed by Spendley et al. (1962) <doi:10.1080/00401706.1962.10490033> and later modified by Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308> for laboratory and manufacturing processes. The package also provides tools for graphical representation of the simplexes and some example response surfaces that are useful in illustrating the optimization process.

Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.

The Programme for International Student Assessment (PISA) is a global study conducted by the Organization for Economic Cooperation and Development (OECD) in member and non-member countries to assess educational systems by assessing 15-year-old school students academic performance in mathematics, science, and reading. This datasets contains information on their scores and other socioeconomic characteristics, information about their school and its infrastructure, as well as the countries that are taking part in the program.

In the fashion of node.js <https://nodejs.org/>, requires a file, sourcing into the current environment only the variables explicitly specified in the module.exports or exports list variable. If the file was already sourced, the result of the earlier sourcing is returned to the caller.

Originally design to characterise Aqueous Two Phase Systems, LLSR provide a simple way to analyse experimental data and obtain phase diagram parameters, among other properties, systematically. The package will include (every other update) new functions in order to comprise useful tools in liquid-liquid extraction research.

This package provides functions to sample from the double log normal distribution and calculate the density, distribution and quantile functions.

Identifying latent genetic interactions in genome-wide association studies using the Latent Interaction Testing (LIT) framework. LIT is a flexible kernel-based approach that leverages information across multiple traits to detect latent genetic interactions without specifying or observing the interacting variable (e.g., environment). LIT accepts standard PLINK files as inputs to analyze large genome-wide association studies.

This package provides a set of functions that allow stationary analysis and locally stationary time series analysis.

Fit linear models based on periodic splines, moderate model coefficients using multivariate adaptive shrinkage, then compute properties of the moderated curves.

Data sets on various litter types like beach litter, riverain litter, floating litter, and seafloor litter are rapidly growing. This package offers a simple user interface to analyse these litter data in a consistent and reproducible way. It also provides functions to facilitate several kinds of litter analysis, e.g., trend analysis, power analysis, and baseline analysis. Under the hood, these functions are also used by the user interface. See Schulz et al. (2019) <doi:10.1016/j.envpol.2019.02.030> for details. MS-Windows users are advised to run litteR in RStudio'. See our vignette: Installation manual for RStudio and litteR'.

This package provides a nonparametric method to approximate Laplacian graph spectra of a network with ordered vertices. This provides a computationally efficient algorithm for obtaining an accurate and smooth estimate of the graph Laplacian basis. The approximation results can then be used for tasks like change point detection, k-sample testing, and so on. The primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).