Train and make predictions from a multi-layer perceptron neural network with optional partial monotonicity constraints.

This is the core package offering a portal to the many packages universe. It includes functions to help researchers access, work across, and maintain ensembles of datasets on global governance called datacubes.

Computes the maximum matching for unweighted graph and maximum matching for (un)weighted bipartite graph efficiently.

This package provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) <doi:10.1111/iere.12131>. Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.

Overcomes one of the major challenges in mobile (passive) sensing, namely being able to pre-process the raw data that comes from a mobile sensing app, specifically m-Path Sense <https://m-path.io>. The main task of mpathsenser is therefore to read m-Path Sense JSON files into a database and provide several convenience functions to aid in data processing.

Monolix is a tool for running mixed effects model using saem'. This tool allows you to convert Monolix models to rxode2 (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) using the form compatible with nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>). If available, the rxode2 model will read in the Monolix data and compare the simulation for the population model individual model and residual model to immediately show how well the translation is performing. This saves the model development time for people who are creating an rxode2 model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a rxode2 model. This is complementary to the babelmixr2 package that translates nlmixr2 models to Monolix and can convert the objects converted from monolix2rx to a full nlmixr2 fit. While not required, you can get/install the lixoftConnectors package in the Monolix installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When lixoftConnectors is available, Monolix can be used to load its model library instead manually setting up text files (which only works with old versions of Monolix').

Bayesian multivariate age-period-cohort (MAPC) models for analyzing health data, with support for model fitting, visualization, stratification, and model comparison. Inference focuses on identifiable cross-strata differences, as described by Riebler and Held (2010) <doi:10.1093/biostatistics/kxp037>. Methods for handling complex survey data via the survey package are included, as described in Mercer et al. (2014) <doi:10.1016/j.spasta.2013.12.001>.

Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2024) <https://grodriguezrondon.com/files/RodriguezRondon_Dufour_2025_MonteCarlo_LikelihoodRatioTest_MarkovSwitchingModels_20251014.pdf> that can be used to identify the number of regimes in Markov switching models.

This package provides a tool for computing probabilities and other quantities that are relevant in selecting performance criteria for discrete trial training. The main function, miebl(), computes Bayesian and frequentist probabilities and bounds for each of n possible performance criterion choices when attempting to determine a student's true mastery level by counting their number of successful attempts at displaying learning among n trials. The reporting function miebl_re() takes output from miebl() and prepares it into a brief report for a specific criterion. miebl_cp() combines 2 to 5 distributions of true mastery level given performance criterion in one plot for comparison. Ramos (2025) <doi:10.1007/s40617-025-01058-9>.

Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.

Data-driven approach for Exploratory Factor Analysis (EFA) that uses Model Implied Instrumental Variables (MIIVs). The method starts with a one factor model and arrives at a suggested model with enhanced interpretability that allows cross-loadings and correlated errors.

Multivariate generalized Gaussian distribution, Multivariate Cauchy distribution, Multivariate t distribution. Distance between two distributions (see N. Bouhlel and A. Dziri (2019): <doi:10.1109/LSP.2019.2915000>, N. Bouhlel and D. Rousseau (2022): <doi:10.3390/e24060838>, N. Bouhlel and D. Rousseau (2023): <doi:10.1109/LSP.2023.3324594>). Manipulation of these multivariate probability distributions. This package replaces mggd', mcauchyd and mstudentd'.

This package provides tools for training, selecting, and evaluating maximum entropy (and standard logistic regression) distribution models. This package provides tools for user-controlled transformation of explanatory variables, selection of variables by nested model comparison, and flexible model evaluation and projection. It follows principles based on the maximum- likelihood interpretation of maximum entropy modeling, and uses infinitely- weighted logistic regression for model fitting. The package is described in Vollering et al. (2019; <doi:10.1002/ece3.5654>).

Function multiroc() can be used for computing and visualizing Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) for multi-class classification problems. It supports both One-vs-One approach by M.Bishop, C. (2006, ISBN:978-0-387-31073-2) and One-vs-All approach by Murphy P., K. (2012, ISBN:9780262018029).

This package provides a method to impute the missingness in categorical data. Details see the paper <doi:10.4310/SII.2020.v13.n1.a2>.

Prediction of behaviour from movement characteristics using observation and random forest for the analyses of movement data in ecology. From movement information (speed, bearing...) the model predicts the observed behaviour (movement, foraging...) using random forest. The model can then extrapolate behavioural information to movement data without direct observation of behaviours. The specificity of this method relies on the derivation of multiple predictor variables from the movement data over a range of temporal windows. This procedure allows to capture as much information as possible on the changes and variations of movement and ensures the use of the random forest algorithm to its best capacity. The method is very generic, applicable to any set of data providing movement data together with observation of behaviour.

The 1001 time series from the M-competition (Makridakis et al. 1982) <DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.

Fit and plot macroecological patterns predicted by the Maximum Entropy Theory of Ecology (METE).

Tests for block-diagonal structure in symmetric matrices (e.g. correlation matrices) under the null hypothesis of exchangeable off-diagonal elements. As described in Segal et al. (2019), these tests can be useful for construct validation either by themselves or as a complement to confirmatory factor analysis. Monte Carlo methods are used to approximate the permutation p-value with Hubert's Gamma (Hubert, 1976) and a t-statistic. This package also implements the chi-squared statistic described by Steiger (1980). Please see Segal, et al. (2019) <doi:10.1007/s11336-018-9647-4> for more information.

Publicly available data from Medicare frequently requires extensive initial effort to extract desired variables and merge them; this package formalizes the techniques I've found work best. More information on the Medicare program, as well as guidance for the publicly available data this package targets, can be found on CMS's website covering publicly available data. See <https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems.html>.

Many useful functions and extensions for dealing with meteorological data in the tidy data framework. Extends ggplot2 for better plotting of scalar and vector fields and provides commonly used analysis methods in the atmospheric sciences.

This package provides a mixed collection of useful and semi-useful diverse statistical functions, some of which may even be referenced in The R Primer book. See Ekstrøm, C. T. (2016). The R Primer. 2nd edition. Chapman & Hall.

This package implements the Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge (MOC-GaPBK) which was proposed by Parraga-Alava, J. et. al. (2018) <doi:10.1186/s13040-018-0178-4>.

Developed for the following tasks. 1- simulating realizations from the canonical, restricted, and unrestricted finite mixture models. 2- Monte Carlo approximation for density function of the finite mixture models. 3- Monte Carlo approximation for the observed Fisher information matrix, asymptotic standard error, and the corresponding confidence intervals for parameters of the mixture models sing the method proposed by Basford et al. (1997) <https://espace.library.uq.edu.au/view/UQ:57525>.