Fast design of risk parity portfolios for financial investment. The goal of the risk parity portfolio formulation is to equalize or distribute the risk contributions of the different assets, which is missing if we simply consider the overall volatility of the portfolio as in the mean-variance Markowitz portfolio. In addition to the vanilla formulation, where the risk contributions are perfectly equalized subject to no shortselling and budget constraints, many other formulations are considered that allow for box constraints and shortselling, as well as the inclusion of additional objectives like the expected return and overall variance. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the papers: Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63, no. 19, pp. 5285-5300. <doi:10.1109/TSP.2015.2452219>. F. Spinu (2013), An Algorithm for Computing Risk Parity Weights. <doi:10.2139/ssrn.2297383>. T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing High-dimensional risk parity portfolios. <arXiv:1311.4057>.

This package provides a robust and powerful approach is developed for replicability analysis of two Genome-wide association studies (GWASs) accounting for the linkage disequilibrium (LD) among genetic variants. The LD structure in two GWASs is captured by a four-state hidden Markov model (HMM). The unknowns involved in the HMM are estimated by an efficient expectation-maximization (EM) algorithm in combination with a non-parametric estimation of functions. By incorporating information from adjacent locations via the HMM, this approach identifies the entire clusters of genotype-phenotype associated signals, improving the power of replicability analysis while effectively controlling the false discovery rate.

This package provides environment modules functionality, which enables use of the Environment Modules system (<http://modules.sourceforge.net/>) from within the R environment. By default the user's login shell environment (ie. "bash -l") will be used to initialize the current session. The module function can also; load or unload specific software, list all the loaded software within the current session, and list all the applications available for loading from the module system. Lastly, the module function can remove all loaded software from the current session.

Work with the Macrostrat (<https://macrostrat.org/>) Web Service (v.2, <https://macrostrat.org/api/v2>) to fetch geological data relevant to the spatial and temporal distribution of sedimentary, igneous, and metamorphic rocks as well as data extracted from them.

This package provides convenience functions to communicate with an Experigen server: Experigen (<http://github.com/aquincum/experigen>) is an online framework for creating linguistic experiments, and it stores the results on a dedicated server. This package can be used to retrieve the results from the server, and it is especially helpful with registered experiments, as authentication with the server has to happen.

This package implements various Riemannian metrics for symmetric positive definite matrices, including AIRM (Affine Invariant Riemannian Metric, see Pennec, Fillard, and Ayache (2006) <doi:10.1007/s11263-005-3222-z>), Log-Euclidean (see Arsigny, Fillard, Pennec, and Ayache (2006) <doi:10.1002/mrm.20965>), Euclidean, Log-Cholesky (see Lin (2019) <doi:10.1137/18M1221084>), and Bures-Wasserstein metrics (see Bhatia, Jain, and Lim (2019) <doi:10.1016/j.exmath.2018.01.002>). Provides functions for computing logarithmic and exponential maps, vectorization, and statistical operations on the manifold of positive definite matrices.

The Kolmogorov-Smirnov (K-S) statistic is a standard method to measure the model strength for credit risk scoring models. This package calculates the Kâ S statistic and plots the true-positive rate and false-positive rate to measure the model strength. This package was written with the credit marketer, who uses risk models in conjunction with his campaigns. The users could read more details from Thrasher (1992) <doi:10.1002/dir.4000060408> and pyks <https://pypi.org/project/pyks/>.

Helper function to install packages for R using an external requirements.txt or a string containing diverse packages from several resources like Github or CRAN.

This function conducts variation partitioning and hierarchical partitioning to calculate the unique, shared (referred as to "common") and individual contributions of each predictor (or matrix) towards explained variation (R-square and adjusted R-square) on canonical analysis (RDA,CCA and db-RDA), applying the algorithm of Lai J.,Zou Y., Zhang J.,Peres-Neto P.(2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution,13: 782-788 <DOI:10.1111/2041-210X.13800>.

This package implements a series of robust Kalman filtering approaches. It implements the additive outlier robust filters of Ruckdeschel et al. (2014) <arXiv:1204.3358> and Agamennoni et al. (2018) <doi:10.1109/ICRA.2011.5979605>, the innovative outlier robust filter of Ruckdeschel et al. (2014) <arXiv:1204.3358>, as well as the innovative and additive outlier robust filter of Fisch et al. (2020) <arXiv:2007.03238>.

This package provides functions for reading, analysing and plotting river networks. For this package, river networks consist of sections and nodes with associated attributes, e.g. to characterise their morphological, chemical and biological state. The package provides functions to read this data from text files, to analyse the network structure and network paths and regions consisting of sections and nodes that fulfill prescribed criteria, and to plot the river network and associated properties.

The goal of rFIA is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, rFIA simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, rFIA improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., dplyr', tidyr', and sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates.

Provide reproducible R chunks in R Markdown document that automatically check computational results for reproducibility. This is achieved by creating json files storing metadata about computational results. A comprehensive tutorial to the package is available as preprint by Brandmaier & Peikert (2024, <doi:10.31234/osf.io/3zjvf>).

Estimates the pooled (unadjusted) Receiver Operating Characteristic (ROC) curve, the covariate-adjusted ROC (AROC) curve, and the covariate-specific/conditional ROC (cROC) curve by different methods, both Bayesian and frequentist. Also, it provides functions to obtain ROC-based optimal cutpoints utilizing several criteria. Based on Erkanli, A. et al. (2006) <doi:10.1002/sim.2496>; Faraggi, D. (2003) <doi:10.1111/1467-9884.00350>; Gu, J. et al. (2008) <doi:10.1002/sim.3366>; Inacio de Carvalho, V. et al. (2013) <doi:10.1214/13-BA825>; Inacio de Carvalho, V., and Rodriguez-Alvarez, M.X. (2022) <doi:10.1214/21-STS839>; Janes, H., and Pepe, M.S. (2009) <doi:10.1093/biomet/asp002>; Pepe, M.S. (1998) <http://www.jstor.org/stable/2534001?seq=1>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1016/j.csda.2010.07.018>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1007/s11222-010-9184-1>. Please see Rodriguez-Alvarez, M.X. and Inacio, V. (2021) <doi:10.32614/RJ-2021-066> for more details.

Includes algorithms to assess research productivity and patterns, such as the h-index and i-index. Cardoso et al. (2022) Cardoso, P., Fukushima, C.S. & Mammola, S. (2022) Quantifying the internationalization and representativeness in research. Trends in Ecology and Evolution, 37: 725-728.

Implementation of the Robust Gauss-Newton (RGN) algorithm, designed for solving optimization problems with a sum of least squares objective function. For algorithm details please refer to Qin et. al. (2018) <doi:10.1029/2017WR022488>.

This package provides a portable Shiny tool to explore patient-level electronic health record data and perform chart review in a single integrated framework. This tool supports browsing clinical data in many different formats including multiple versions of the OMOP common data model as well as the MIMIC-III data model. In addition, chart review information is captured and stored securely via the Shiny interface in a REDCap (Research Electronic Data Capture) project using the REDCap API. See the ReviewR website for additional information, documentation, and examples.

This package provides tools to perform random forest consensus clustering of different data types. The package is designed to accept a list of matrices from different assays, typically from high-throughput molecular profiling so that class discovery may be jointly performed. For references, please see Tao Shi & Steve Horvath (2006) <doi:10.1198/106186006X94072> & Monti et al (2003) <doi:10.1023/A:1023949509487> .

Create an R Journal Rmarkdown template article, that will generate html and pdf versions of your paper. Check that the paper folder has all the required components needed for submission. Examples of R Journal publications can be found at <https://journal.r-project.org>.

Empirical orthogonal teleconnections in R. remote is short for R(-based) EMpirical Orthogonal TEleconnections'. It implements a collection of functions to facilitate empirical orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections (EOTs) denote a regression based approach to decompose spatio-temporal fields into a set of independent orthogonal patterns. They are quite similar to Empirical Orthogonal Functions (EOFs) with EOTs producing less abstract results. In contrast to EOFs, which are orthogonal in both space and time, EOT analysis produces patterns that are orthogonal in either space or time.

Enhances the R Optimization Infrastructure ('ROI') package by registering the SYMPHONY open-source solver from the COIN-OR suite. It allows for solving mixed integer linear programming (MILP) problems as well as all variants/combinations of LP, IP.

Queries data from WHOIS servers.

This package performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) <https://ilykei.com>.

This package provides a straightforward model to estimate soil migration rates across various soil contexts. Based on the compartmental, vertically-resolved, physically-based mass balance model of Soto and Navas (2004) <doi:10.1016/j.jaridenv.2004.02.003> and Soto and Navas (2008) <doi:10.1016/j.radmeas.2008.02.024>. RadEro provides a user-friendly interface in R, utilizing input data such as 137Cs inventories and parameters directly derived from soil samples (e.g., fine fraction density, effective volume) to accurately capture the 137Cs distribution within the soil profile. The model simulates annual 137Cs fallout, radioactive decay, and vertical diffusion, with the diffusion coefficient calculated from 137Cs reference inventory profiles. Additionally, it allows users to input custom parameters as calibration coefficients. The RadEro user manual and protocol, including detailed instructions on how to format input data and configuration files, can be found at the following link: <https://github.com/eead-csic-eesa/RadEro>.