knitr, MplusAutomation (>= 0.7), rmarkdown
lavaan (>= 0.5-22), ggplot2, tidyr, stringr

c(person("Anthony", "Raborn", email = "anthony.w.raborn@gmail.com", role = c("aut", "cre")), person("Walter", "Leite", email = "Walter.Leite@coe.ufl.edu", role = "aut"))
Anthony Raborn [aut, cre],
Walter Leite [aut]

This package provides nonparametric Stein-type shrinkage estimators of the covariance matrix that are suitable in high-dimensional settings, that is when the number of variables is larger than the sample size.

This package provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.

The package ShrinkCovMat provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.

Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.

person("Anestis", "Touloumis", role = c("aut", "cre"),
email = "A.Touloumis@brighton.ac.uk",
comment = c(ORCHID = "0000-0002-5965-1639"))

Corey M. Yanofsky, Zahra Montazeri, Marta Padilla, Alaa Ali and David R. Bickel
Several shrinkage estimators of the effect-size of a parameter of interest.
R (>= 2.14.0), methods, PsiHat, multtest, limma

Signal Extraction Approach for Sparse Multivariate Response
Regression
Methods for regression with high-dimensional predictors and  univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.

Individual-Based, Spatially-Explicit Simulation and Analysis of
Multi-State Movements in River Networks and Heterogeneous
Landscapes
c(person("Lorenzo", "Quaglietta", role = c("aut"), email = "lontrenzo@gmail.com")
,person("Miguel", "Porto", role = c("aut", "cre"), email = "mpbertolo@gmail.com")
,person("Erida", "Gjini", role = c("ctb"), email = "egjini@igc.gulbenkian.pt")
)
Provides functions to generate and analyze individual-based spatially-explicit
simulations of multi-state movements in heterogeneous landscapes, based on "resistance"
rasters. Although originally conceived and designed to fill the gap of softwares simulating
spatially-explicit trajectories of species constrained to linear, dendritic habitats
(e.g., river networks), the simulation algorithm is built to be highly flexible and can be
applied to any (aquatic, semi-aquatic or terrestrial) organism. Thus, the user will be able
to use the package to simulate movements either in homogeneous landscapes, heterogeneous
landscapes (e.g. semi-aquatic animal in a riverscape), or even in highly contrasted
landscapes (e.g. fish in a river network). The algorithm and its input parameters are
the same for all cases, so that results are comparable. Simulated trajectories can then
be used as null models to test e.g. for species site fidelity and other movement ecology
hypotheses, or to build predictive, mechanistic movement models, among other things. The
package should thus be relevant to explore a broad spectrum of ecological phenomena, such
as those at the interface of animal behaviour, landscape, spatial and movement ecology,
disease and invasive species spread, and population dynamics. This is the first released
experimental version; do test before using in production.
Lorenzo Quaglietta [aut],
Miguel Porto [aut, cre],
Erida Gjini [ctb]