This package provides functions for computing split regularized estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2019) <doi:10.48550/arXiv.1712.03561>. The approach fits linear regression models that split the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit.

This package provides a template system based on AdminLTE3 (<https://adminlte.io/themes/v3/>) theme. Comes with default theme that can be easily customized. Developers can upload modified templates on Github', and users can easily download templates with RStudio project wizard. The key features of the default template include light and dark theme switcher, resizing graphs, synchronizing inputs across sessions, new notification system, fancy progress bars, and card-like flip panels with back sides, as well as various of HTML tool widgets.

Provide data generation and estimation tools for the multivariate scale mixtures of normal presented in Lange and Sinsheimer (1993) <doi:10.2307/1390698>, the multivariate scale mixtures of skew-normal presented in Zeller, Lachos and Vilca (2011) <doi:10.1080/02664760903406504>, the multivariate skew scale mixtures of normal presented in Louredo, Zeller and Ferreira (2021) <doi:10.1007/s13571-021-00257-y> and the multivariate scale mixtures of skew-normal-Cauchy presented in Kahrari et al. (2020) <doi:10.1080/03610918.2020.1804582>.

You only need to type why pie charts are bad on Google to find thousands of articles full of (valid) reasons why other types of charts should be preferred over this one. Therefore, because of the little use due to the reasons already mentioned, making pie charts (and related) in R is not straightforward, so other functions are needed to simplify things. In this R package there are useful functions to make tasty pie charts immediately by exploiting the many cool templates provided.

Imports Variant Calling Format file into R. It can detect whether a sample contains contaminant from the same species. In the first stage of the approach, a change-point detection method is used to identify copy number variations for filtering. Next, features are extracted from the data for a support vector machine model. For log-likelihood calculation, the deviation parameter is estimated by maximum likelihood method. Using a radial basis function kernel support vector machine, the contamination of a sample can be detected.

This package provides a collection of several utility functions related to resolvable and affine resolvable Partially Balanced Incomplete Block Designs (PBIBDs), have been developed. In the class of resolvable designs, affine resolvable designs are said to be optimal, Bailey (1995) <doi:10.2307/2337638>. Here, the package contains three functions to generate and study the characterization properties of these designs. Developed functions are named as PBIBD1(), PBIBD2() and PBIBD3(), in which first two functions are used to generate two new series of affine resolvable PBIBDs and last one is used to generate a new series of resolvable PBIBDs, respectively. In addition, these functions can also be used to generate design parameters (v, b, r and k), canonical efficiency factors, variance factor between associates and average variance factors of the generated designs. Here v is the number of treatments, b (= b1 + b2, in case of non-proper design) is the number of blocks, r is the number of replications and k (= k1 + k2; k1 is the size of b1 and k2 is the size of b2) is the block size.

Evaluate, fit, and analyze Hill dose response models (Goutelle et al., 2008 <doi:10.1111/j.1472-8206.2008.00633.x>), also sometimes referred to as four-parameter log-logistic models. Includes tools to invert Hill models, select models based on the Akaike information criterion (Akaike, 1974 <doi:10.1109/TAC.1974.1100705>) or Bayesian information criterion (Schwarz, 1978 <https://www.jstor.org/stable/2958889>), and construct bootstrapped confidence intervals both on the Hill model parameters and values derived from the Hill model parameters.

The level-dependent cross-validation method is implemented for the selection of thresholding value in wavelet shrinkage. This procedure is implemented by coupling a conventional cross validation with an imputation method due to a limitation of data length, a power of 2. It can be easily applied to classical leave-one-out and k-fold cross validation. Since the procedure is computationally fast, a level-dependent cross validation can be performed for wavelet shrinkage of various data such as a data with correlated errors.

This package provides tools for storing and managing competition results. Competition is understood as a set of games in which players gain some abstract scores. There are two ways for storing results: in long (one row per game-player) and wide (one row per game with fixed amount of players) formats. This package provides functions for creation and conversion between them. Also there are functions for computing their summary and Head-to-Head values for players. They leverage grammar of data manipulation from dplyr'.

This package provides a modular package for measuring disparity (multidimensional space occupancy). Disparity can be calculated from any matrix defining a multidimensional space. The package provides a set of implemented metrics to measure properties of the space and allows users to provide and test their own metrics. The package also provides functions for looking at disparity in a serial way (e.g. disparity through time) or per groups as well as visualising the results. Finally, this package provides several statistical tests for disparity analysis.

Nonparametric smoothing techniques for data on a lattice and functional time series. Smoothing is done via kernel regression or local polynomial regression, a bandwidth selection procedure based on an iterative plug-in algorithm is implemented. This package allows for modeling a dependency structure of the error terms of the nonparametric regression model. Methods used in this paper are described in Feng/Schaefer (2021) <https://ideas.repec.org/p/pdn/ciepap/144.html>, Schaefer/Feng (2021) <https://ideas.repec.org/p/pdn/ciepap/143.html>.

Estimation of four-fold table cell frequencies (raw data) from risk ratios (relative risks), risk differences and odds ratios. While raw data can be useful for doing meta-analysis, such data is often not provided by primary studies (with summary statistics being solely presented). Therefore, based on summary statistics (namely, risk ratios, risk differences and odds ratios), this package estimates the value of each cell in a 2x2 table according to the equations described in Di Pietrantonj C (2006) <doi:10.1002/sim.2287>.

Supports teaching methods of estimating and testing time series factor models for use in robust portfolio construction and analysis. Unique in providing not only classical least squares, but also modern robust model fitting methods which are not much influenced by outliers. Includes returns and risk decompositions, with user choice of standard deviation, value-at-risk, and expected shortfall risk measures. "Robust Statistics Theory and Methods (with R)", R. A. Maronna, R. D. Martin, V. J. Yohai, M. Salibian-Barrera (2019) <doi:10.1002/9781119214656>.

This package provides a model for leaf fluorescence, reflectance and transmittance spectra. It implements the model introduced by Vilfan et al. (2016) <DOI:10.1016/j.rse.2016.09.017>. Fluspect-B calculates the emission of ChlF on both the illuminated and shaded side of the leaf. Other input parameters are chlorophyll and carotenoid concentrations, leaf water, dry matter and senescent material (brown pigments) content, leaf mesophyll structure parameter and ChlF quantum efficiency for the two photosystems, PS-I and PS-II.

Builds a LASSO, Ridge, or Elastic Net model with glmnet or cv.glmnet with bootstrap inference statistics (SE, CI, and p-value) for selected coefficients with no shrinkage applied for them. Model performance can be evaluated on test data and an automated alpha selection is implemented for Elastic Net. Parallelized computation is used to speed up the process. The methods are described in Friedman et al. (2010) <doi:10.18637/jss.v033.i01> and Simon et al. (2011) <doi:10.18637/jss.v039.i05>.

Environmental health studies are increasingly measuring multiple pollutants to characterize the joint health effects attributable to exposure mixtures. However, the underlying dose-response relationship between toxicants and health outcomes of interest may be highly nonlinear, with possible nonlinear interaction effects. Hierarchical integrative group least absolute shrinkage and selection operator (HiGLASSO), developed by Boss et al (2020) <arXiv:2003.12844>, is a general framework to identify noteworthy nonlinear main and interaction effects in the presence of group structures among a set of exposures.

This package provides two functions that implement the one-sided and two-sided versions of the Hodrick-Prescott filter. The one-sided version is a Kalman filter-based implementation, whereas the two- sided version uses sparse matrices for improved efficiency. References: Hodrick, R. J., and Prescott, E. C. (1997) <doi:10.2307/2953682> Mcelroy, T. (2008) <doi:10.1111/j.1368-423X.2008.00230.x> Meyer-Gohde, A. (2010) <https://ideas.repec.org/c/dge/qmrbcd/181.html> For more references, see the vignette.

This package provides access to granular socioeconomic indicators from the Spanish Statistical Office (INE) Household Income Distribution Atlas. The package downloads and processes data from a companion GitHub repository (<https://github.com/pablogguz/ineAtlas.data/>) which contains processed versions of the official INE Atlas data. Functions are provided to fetch data at multiple geographic levels (municipalities, districts, and census tracts), including income indicators, demographic characteristics, and inequality metrics. The data repository is updated every year when new releases are published by INE.

This package contains procedures to estimate the nine condensed Jacquard genetic identity coefficients (Jacquard, 1974) <doi:10.1007/978-3-642-88415-3> by constrained least squares (Graffelman et al., 2024) <doi:10.1101/2024.03.25.586682> and by the method of moments (Csuros, 2014) <doi:10.1016/j.tpb.2013.11.001>. These procedures require previous estimation of the allele frequencies. Functions are supplied that estimate relationship parameters that derive from the Jacquard coefficients, such as individual inbreeding coefficients and kinship coefficients.

Download data from the Ada and Archibald MacLeish Field Station in Whately, MA. The Ada and Archibald MacLeish Field Station is a 260-acre patchwork of forest and farmland located in West Whately, MA that provides opportunities for faculty and students to pursue environmental research, outdoor education, and low-impact recreation (see <https://www.smith.edu/about-smith/sustainable-smith/macleish> for more information). This package contains weather data over several years, and spatial data on various man-made and natural structures.

Generates n hierarchical clustering hypotheses on subsets of classifiers (usually species in community ecology studies). The n clustering hypotheses are combined to generate a generalized cluster, and computes three metrics of support. 1) The average proportion of elements conforming the group in each of the n clusters (integrity). And 2) the contamination, i.e., the average proportion of elements from other groups that enter a focal group. 3) The probability of existence of the group gives the integrity and contamination in a Bayesian approach.

Accurate classification of breast cancer tumors based on gene expression data is not a trivial task, and it lacks standard practices.The PAM50 classifier, which uses 50 gene centroid correlation distances to classify tumors, faces challenges with balancing estrogen receptor (ER) status and gene centering. The PCAPAM50 package leverages principal component analysis and iterative PAM50 calls to create a gene expression-based ER-balanced subset for gene centering, avoiding the use of protein expression-based ER data resulting into an enhanced Breast Cancer subtyping.

Portable /proc/self/maps as a data frame. Determine which library or other region is mapped to a specific address of a process. -- R packages can contain native code, compiled to shared libraries at build or installation time. When loaded, each shared library occupies a portion of the address space of the main process. When only a machine instruction pointer is available (e.g. from a backtrace during error inspection or profiling), the address space map determines which library this instruction pointer corresponds to.

Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) <http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf>); parametric simulation from NONMEM-generated parameter estimates and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN).