Auxiliary Functions to Estimate Centers of Biodiversity

Provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.

Rcpp Hidden Markov Model

Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.

Download Data from the Wittgenstein Centre Human Capital Data Explorer

Download and plot education specific demographic data from the Wittgenstein Centre for Demography and Human Capital Data Explorer < http://dataexplorer.wittgensteincentre.org/>.

Blind Source Separation for Multivariate Spatio-Temporal Data

Simultaneous/joint diagonalization of local autocovariance matrices to estimate spatio-temporally uncorrelated random fields.

Simple Tools for Defining Species Ranges

A collection of tools to create species range maps based on occurrence data, statistics, and spatial objects. Other tools in this collection can be used to analyze the environmental characteristics of the species ranges. Plotting options to represent results in various manners are also available. Results obtained using these tools can be used to explore the distribution of species and define areas of occupancy and extent of occurrence of species. Other packages help to explore species distributions using distinct methods, but options presented in this set of tools (e.g., using trend surface analysis and concave hull polygons) are exclusive. Description of methods, approaches, and comments for some of the tools implemented here can be found in: IUCN (2001) < https://portals.iucn.org/library/node/10315>, Peterson et al. (2011) < https://www.degruyter.com/princetonup/view/title/506966>, and Graham and Hijmans (2006) .

A Phylogenetic Simulator for Reticulate Evolution

A simulator for reticulate evolution under a birth-death-hybridization process. Here the birth-death process is extended to consider reticulate Evolution by allowing hybridization events to occur. The general purpose simulator allows the modeling of three different reticulate patterns: lineage generative hybridization, lineage neutral hybridization, and lineage degenerative hybridization. Users can also specify hybridization events to be dependent on a trait value or genetic distance. We also extend some phylogenetic tree utility and plotting functions for networks. We allow two different stopping conditions: simulated to a fixed time or number of taxa. When simulating to a fixed number of taxa, the user can simulate under the Generalized Sampling Approach that properly simulates phylogenies when assuming a uniform prior on the root age.

Statistical Methods for Survival Data with Dependent Censoring

Several statistical methods for analyzing survival data under various forms of dependent censoring are implemented in the package. In addition to accounting for dependent censoring, it offers tools to adjust for unmeasured confounding factors. The implemented approaches allow users to estimate the dependency between survival time and dependent censoring time, based solely on observed survival data. For more details on the methods, refer to Deresa and Van Keilegom (2021) , Czado and Van Keilegom (2023) , Crommen et al. (2024) , Deresa and Van Keilegom (2024) , Rutten et al. (2024+) and Ding and Van Keilegom (2024).

Test Data for the 'admiral' Package

A set of Study Data Tabulation Model (SDTM) datasets from the Clinical Data Interchange Standards Consortium (CDISC) pilot project used for testing and developing Analysis Data Model (ADaM) derivations inside the 'admiral' package.

Blind Source Separation for Multivariate Spatial Data

Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) .

Conditional Maximum Likelihood for Quadratic Exponential Models for Binary Panel Data

Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2018, Econometric Reviews) . The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics) . For large time dimensions of the panel, the computation of the proposed models involves a recursive function from Krailo M. D., & Pike M. C. (1984, Journal of the Royal Statistical Society. Series C (Applied Statistics)) and Bartolucci F., Valentini, F. & Pigini C. (2021, Computational Economics .