This package provides a joint latent class model where a hierarchical structure exists, with an interaction between female and male partners of a couple. A Bayesian perspective to inference and Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. The reference paper is: Beom Seuk Hwang, Zhen Chen, Germaine M.Buck Louis, Paul S. Albert, (2018) "A Bayesian multi-dimensional couple-based latent risk model with an application to infertility". Biometrics, 75, 315-325. <doi:10.1111/biom.12972>.

Conveniently generate CSS using R code.

An adaption of the consensus clustering approach from ConsensusClusterPlus for longitudinal data. The longitudinal data is clustered with flexible mixture models from flexmix', while the consensus matrices are hierarchically clustered as in ConsensusClusterPlus'. By using the flexibility from flexmix and FactoMineR', one can use mixed data types for the clustering.

Sparklines are small plots (about one line of text high), made popular by Edward Tufte. This package is the interface from R to the LaTeX package sparklines by Andreas Loeffer and Dan Luecking (<http://www.ctan.org/pkg/sparklines>). It can work with Sweave or knitr or other engines that produce TeX. The package can be used to plot vectors, matrices, data frames, time series (in ts or zoo format).

This package provides tools for sensitivity analysis of LSD simulation models. Reads object-oriented data produced by LSD simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) <doi:10.1016/j.ejor.2007.10.013>) is estimated using the simulation data to provide the data required by the Sobol decomposition. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at <https://www.labsimdev.org/>).

Use of this package is deprecated. It has been renamed to LifeInsureR'.

This package provides tools for detecting and correcting sample mix-ups between two sets of measurements, such as between gene expression data on two tissues. Broman et al. (2015) <doi:10.1534/g3.115.019778>.

We present a method based on filtering algorithms to estimate the parameters of linear, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods. Parameters of logistic regression models are also estimated using an evolutionary particle filter method.

Allows identification of palettes derived from LTER (Long Term Ecological Research) photographs based on user criteria. Also facilitates extraction of palettes from users photos directly.

Create custom labels, badges, certificates and other documents. Automate the production of potentially large numbers of herbarium and collection labels, accreditation badges, attendance and participation certificates, etc, and deliver them automatically. Documents are generated in PDF format, which requires a working installation of LaTeX', such as TinyTeX'.

It uses phenological and productivity-related variables derived from time series of vegetation indexes, such as the Normalized Difference Vegetation Index, to assess ecosystem dynamics and change, which eventually might drive to land degradation. The final result of the Land Productivity Dynamics indicator is a categorical map with 5 classes of land productivity dynamics, ranging from declining to increasing productivity. See www.sciencedirect.com/science/article/pii/S1470160X21010517/ for a description of the methods used in the package to calculate the indicator.

Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is logConDens that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.

Estimation of Latent Order Logistic (LOLOG) Models for Networks. LOLOGs are a flexible and fully general class of statistical graph models. This package provides functions for performing MOM, GMM and variational inference. Visual diagnostics and goodness of fit metrics are provided. See Fellows (2018) <doi:10.48550/arXiv.1804.04583> for a detailed description of the methods.

This package implements a Gibbs sampler to do linear regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic scatter, and a covariate prior distribution which is given by either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base distribution. Described further in Mantz (2016) <DOI:10.1093/mnras/stv3008>.

Compute and visualize using the visNetwork package all the bivariate correlations of a dataframe. Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau, distance correlation, maximal information coefficient and equal-freq discretization-based maximal normalized mutual information) are used according to the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).

Computes the Lomb-Scargle Periodogram and actogram for evenly or unevenly sampled time series. Includes a randomization procedure to obtain exact p-values. Partially based on C original by Press et al. (Numerical Recipes) and the Python module Astropy. For more information see Ruf, T. (1999). The Lomb-Scargle periodogram in biological rhythm research: analysis of incomplete and unequally spaced time-series. Biological Rhythm Research, 30(2), 178-201.

Convenient aliases for common ways of misspelling the base R function length(). These include every permutation of the final three letters.

Calculates cost surfaces based on slope to be used when modelling pathways and movement potential within a landscape (Lewis, 2021) <doi:10.1007/s10816-021-09522-w>.

In addition to modeling the expectation (location) of an outcome, mixed effects location scale models (MELSMs) include submodels on the variance components (scales) directly. This allows models on the within-group variance with mixed effects, and between-group variances with fixed effects. The MELSM can be used to model volatility, intraindividual variance, uncertainty, measurement error variance, and more. Multivariate MELSMs (MMELSMs) extend the model to include multiple correlated outcomes, and therefore multiple locations and scales. The latent multivariate MELSM (LMMELSM) further includes multiple correlated latent variables as outcomes. This package implements two-level mixed effects location scale models on multiple observed or latent outcomes, and between-group variance modeling. Williams, Martin, Liu, and Rast (2020) <doi:10.1027/1015-5759/a000624>. Hedeker, Mermelstein, and Demirtas (2008) <doi:10.1111/j.1541-0420.2007.00924.x>.

Estimate haplotypic or composite pairwise linkage disequilibrium (LD) in polyploids, using either genotypes or genotype likelihoods. Support is provided to estimate the popular measures of LD: the LD coefficient D, the standardized LD coefficient D', and the Pearson correlation coefficient r. All estimates are returned with corresponding standard errors. These estimates and standard errors can then be used for shrinkage estimation. The main functions are ldfast(), ldest(), mldest(), sldest(), plot.lddf(), format_lddf(), and ldshrink(). Details of the methods are available in Gerard (2021a) <doi:10.1111/1755-0998.13349> and Gerard (2021b) <doi:10.1038/s41437-021-00462-5>.

Perform pairwise likelihood inference in latent autoregressive count models. See Pedeli and Varin (2020) for details.

Local Polynomial Regression with Ridging.

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.

This package implements bootstrap methods for linear regression models with errors following a time-varying process, focusing on approximating the distribution of the least-squares estimator for regression models with locally stationary errors. It enables the construction of bootstrap and classical confidence intervals for regression coefficients, leveraging intensive simulation studies and real data analysis.