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Model and Analyse Interval Data
Implements methodologies for modelling interval data by Normal and Skew-Normal distributions, considering appropriate parameterizations of the variance-covariance matrix that takes into account the intrinsic nature of interval data, and lead to four different possible configuration structures. The Skew-Normal parameters can be estimated by maximum likelihood, while Normal parameters may be estimated by maximum likelihood or robust trimmed maximum likelihood methods.
Validation of Arguments and Objects in User-Defined Functions
Utility functions that implement and automate common sets of validation tasks. These functions are particularly useful to validate inputs, intermediate objects and output values in user-defined functions, resulting in tidier and less verbose functions.
Computational Tools for Economics
Implements solutions to canonical models of Economics such as Monopoly Profit Maximization, Cournot's Duopoly, Solow (1956,
Infer Gene Probabilistic Boolean Networks from Single-Cell Data
Given a gene regulatory boolean network and a RNA-seq dataset,
this package computes protein activity normalised enrichment scores
using 'VIPER', and then produces a probabilistic network using the scores
as probabilities for fixed node activation or deactivation,
in addition to the original Boolean functions.
For more information, refer to the preprint:
Victori and Buffa (2022)
Bayes Linear Estimators for Finite Population
Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary information) - BLE_Ratio() - and to categorical data - BLE_Categorical(). The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg() and it is also possible to achieve the design based estimators using vague prior distributions. Based on Gonçalves, K.C.M, Moura, F.A.S and Migon, H.S.(2014) < https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.
Selecting Variable Subsets
A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria. Theoretical support for the heuristic search methods and exploratory data analysis criteria is in Cadima, Cerdeira, Minhoto (2003,
Efficient Outlier Detection for Large Time Series Databases
Programs for detecting and cleaning outliers in single time series and in time series from homogeneous and heterogeneous databases using an Orthogonal Greedy Algorithm (OGA) for saturated linear regression models. The programs implement the procedures presented in the paper entitled "Efficient Outlier Detection for Large Time Series Databases" by Pedro Galeano, Daniel Peña and Ruey S. Tsay (2026), working paper, Universidad Carlos III de Madrid. Version 1.1.2 fixes one bug.
Aggregate Multiple ROC Curves into One Global ROC
Aggregates multiple Receiver Operating Characteristic (ROC) curves obtained from different sources into one global ROC. Additionally, it’s also possible to calculate the aggregated precision-recall (PR) curve.
Helper Functions for Species Delimitation Analysis
Helpers functions to process, analyse, and visualize the output of single locus species delimitation methods. For full functionality, please install suggested software at < https://legallab.github.io/delimtools/articles/install.html>.
Robust Selection Algorithm
An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) < http://proceedings.mlr.press/v108/cisneros20a.html>.