Compute permutation- based performance measures and create partial dependence plots for (cross-validated) randomForest and ada models.

Get open statistical data and metadata disseminated by the National Statistics Institute of Spain (INE). The functions return data frames with the requested information thanks to calls to the INE API <https://www.ine.es/dyngs/DAB/index.htm?cid=1100>.

An efficient and incremental approach for calculating the differences in orbit counts when performing single edge modifications in a network. Calculating the differences in orbit counts is much more efficient than recalculating all orbit counts from scratch for each time point.

This package provides tools to analyze point patterns in space occurring over planar network structures derived from graph-related intensity measures for undirected, directed, and mixed networks. This package is based on the following research: Eckardt and Mateu (2018) <doi:10.1080/10618600.2017.1391695>. Eckardt and Mateu (2021) <doi:10.1007/s11749-020-00720-4>.

Fit a predictive model using iteratively reweighted boosting (IRBoost) to minimize robust loss functions within the CC-family (concave-convex). This constitutes an application of iteratively reweighted convex optimization (IRCO), where convex optimization is performed using the functional descent boosting algorithm. IRBoost assigns weights to facilitate outlier identification. Applications include robust generalized linear models and robust accelerated failure time models. Wang (2025) <doi:10.6339/24-JDS1138>.

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>.

Multi-data type subtyping, which is data type agnostic and accepts missing data. Subtyping is performed using intermediary assessments created with autoencoders and similarity calculations. See Fox et al. (2024) <doi:10.1016/j.crmeth.2024.100884> for details.

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.

Pre-processing and basic analytical tasks for working with Eurostat's symmetric inputâ output tables, and basic inputâ output economics calculations. Part of rOpenGov <https://ropengov.github.io/> for open source open government initiatives.

Survival analysis of interval-censored data with proportional hazards, and an explicit smooth estimate of the baseline log-hazard with P-splines.

This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.

This package provides a general-purpose workflow for image segmentation using TensorFlow models based on the U-Net architecture by Ronneberger et al. (2015) <arXiv:1505.04597> and the U-Net++ architecture by Zhou et al. (2018) <arXiv:1807.10165>. We provide pre-trained models for assessing canopy density and understory vegetation density from vegetation photos. In addition, the package provides a workflow for easily creating model input and model architectures for general-purpose image segmentation based on grayscale or color images, both for binary and multi-class image segmentation.

This package provides a tool to calculate the performance of a time series in a specific date or period. It is more intended for data analysis in the fields of finance, banking, telecommunications or operational marketing.

The Inductive Subgroup Comparison Approach ('ISCA') offers a way to compare groups that are internally differentiated and heterogeneous. It starts by identifying the social structure of a reference group against which a minority or another group is to be compared, yielding empirical subgroups to which minority members are then matched based on how similar they are. The modelling of specific outcomes then occurs within specific subgroups in which majority and minority members are matched. ISCA is characterized by its data-driven, probabilistic, and iterative approach and combines fuzzy clustering, Monte Carlo simulation, and regression analysis. ISCA_random_assignments() assigns subjects probabilistically to subgroups. ISCA_clustertable() provides summary statistics of each cluster across iterations. ISCA_modeling() provides Ordinary Least Squares regression results for each cluster across iterations. For further details please see Drouhot (2021) <doi:10.1086/712804>.

We construct the explicit form of clamped cubic interpolating spline (both uniform - knots are equidistant and non-uniform - knots are arbitrary). Using this form, we propose a linear regression model suitable for real data smoothing.

Implementation of some Individual Based Models (IBMs, sensu Grimm and Railsback 2005) and methods to create new ones, particularly for population dynamics models (reproduction, mortality and movement). The basic operations for the simulations are implemented in Rcpp for speed.

This package provides a suite of convenient tools for social network analysis geared toward students, entry-level users, and non-expert practitioners. â ideanetâ features unique functions for the processing and measurement of sociocentric and egocentric network data. These functions automatically generate node- and system-level measures commonly used in the analysis of these types of networks. Outputs from these functions maximize the ability of novice users to employ network measurements in further analyses while making all users less prone to common data analytic errors. Additionally, â ideanetâ features an R Shiny graphic user interface that allows novices to explore network data with minimal need for coding.

This package provides tools for passing messages between R processes. Shiny examples are provided showing how to perform useful tasks such as: updating reactive values from within a future, progress bars for long running async tasks, and interrupting async tasks based on user input.

Enables Python'-like importing/loading of packages or functions with aliasing to prevent namespace conflicts.

This package provides functions read a dataframe containing one or more International Classification of Diseases Tenth Revision codes per subject. They return original data with injury categorizations and severity scores added.

This package provides S4 classes for Internet Protocol (IP) versions 4 and 6 addresses and efficient methods for IP addresses comparison, arithmetic, bit manipulation and lookup. Both IPv4 and IPv6 arbitrary ranges are also supported as well as internationalized ('IDN') domain lookup with and whois query.

In classification problems a monotone relation between some predictors and the classes may be assumed. In this package isoboost we propose new boosting algorithms, based on LogitBoost, that incorporate this isotonicity information, yielding more accurate and easily interpretable rules.

This package provides the dataset and an implementation of the method illustrated in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.

Nonparametric estimation on survival analysis under order-restrictions.