Fetch data on targeted public investments from Plataforma +Brasil (SICONV) <http://plataformamaisbrasil.gov.br/>, the responsible system for requests, execution, and monitoring of federal discretionary transfers in Brazil.

An Object-oriented Framework for Geostatistical Modeling in S+ containing functions for variogram estimation, variogram fitting and kriging as well as some plot functions. Written entirely in S, therefore works only for small data sets in acceptable computing time.

Algorithms for fitting scaled sparse linear regression and estimating precision matrices.

Fitting of non-parametric production frontiers for use in efficiency analysis. Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for multiple inputs and a single output using constrained kernel smoothing as in Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints on the estimated frontier.

Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>.

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.

User-friendly framework that enables the training and the evaluation of species distribution models (SDMs). The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the RStudio viewer pane during their execution.

We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. See Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford, Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical Association, 109:505, 11-23, <doi:10.1080/01621459.2013.870904>.

This package provides a graphical user interface to the seasonal package and X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software.

Data used in Taback, N. (2022). Design and Analysis of Experiments and Observational Studies using R. Chapman & Hall/CRC.

This package provides functions that facilitate and speed up the analysis of data produced by a Syntech servosphere <http://www.ockenfels-syntech.com/products/locomotion-compensation/>, which is equipment for studying the movement behavior of arthropods. This package is designed to make working with data produced from a servosphere easy for someone new to or unfamiliar with R. The functions provided in this package fall into three broad-use categories: functions for cleaning raw data produced by the servosphere software, functions for deriving movement variables based on position data, and functions for summarizing movement variables for easier analysis. These functions are built with functions from the tidyverse package to work efficiently, as a single servosphere file may consist of hundreds of thousands of rows of data and a user may wish to analyze hundreds of files at a time. Many of the movement variables derivable through this package are described in the following papers: Otálora-Luna, Fernando; Dickens, Joseph C. (2011) <doi:10.1371/journal.pone.0020990> Party, Virginie; Hanot, Christophe; Busser, Daniela Schmidt; Rochat, Didier; Renou, Michel (2013) <doi:10.1371/journal.pone.0052897> Bell, William J.; Kramer, Ernest (1980) <doi:10.1007/BF01402908> Becher, Paul G; Guerin, Patrick M. (2009) <doi:10.1016/j.jinsphys.2009.01.006>.

An implementation of split-population duration regression models. Unlike regular duration models, split-population duration models are mixture models that accommodate the presence of a sub-population that is not at risk for failure, e.g. cancer patients who have been cured by treatment. This package implements Weibull and Loglogistic forms for the duration component, and focuses on data with time-varying covariates. These models were originally formulated in Boag (1949) and Berkson and Gage (1952), and extended in Schmidt and Witte (1989).

Bayesian regression tree models with shrinkage priors on step heights. Supports continuous, binary, and right-censored (survival) outcomes. Used for high-dimensional prediction and causal inference.

This package implements the S-type estimators, novel robust estimators for general linear regression models, addressing challenges such as outlier contamination and leverage points. This package introduces robust regression techniques to provide a robust alternative to classical methods and includes diagnostic tools for assessing model fit and performance. The methodology is based on the study, "Comparison of the Robust Methods in the General Linear Regression Model" by Sazak and Mutlu (2023). This package is designed for statisticians and applied researchers seeking advanced tools for robust regression analysis.

Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see <doi:10.1145/342009.335388>.

Bayesian estimation for undirected graphical models using spike-and-slab priors. The package handles continuous, discrete, and mixed data.

Preview spatial data as leaflet maps with minimal effort. smartmap is optimized for interactive use and distinguishes itself from similar packages because it does not need real spatial ('sp or sf') objects an input; instead, it tries to automatically coerce everything that looks like spatial data to sf objects or leaflet maps. It - for example - supports direct mapping of: a vector containing a single coordinate pair, a two column matrix, a data.frame with longitude and latitude columns, or the path or URL to a (possibly compressed) shapefile'.

Estimate the abundance of cell clones from the distribution of lengths of DNA fragments (as created by sonication, whence `sonicLength'). The algorithm in "Estimating abundances of retroviral insertion sites from DNA fragment length data" by Berry CC, Gillet NA, Melamed A, Gormley N, Bangham CR, Bushman FD. Bioinformatics; 2012 Mar 15;28(6):755-62 is implemented. The experimental setting and estimation details are described in detail there. Briefly, integration of new DNA in a host genome (due to retroviral infection or gene therapy) can be tracked using DNA sequencing, potentially allowing characterization of the abundance of individual cell clones bearing distinct integration sites. The locations of integration sites can be determined by fragmenting the host DNA (via sonication or fragmentase), breaking the newly integrated DNA at a known sequence, amplifying the fragments containing both host and integrated DNA, sequencing those amplicons, then mapping the host sequences to positions on the reference genome. The relative number of fragments containing a given position in the host genome estimates the relative abundance of cells hosting the corresponding integration site, but that number is not available and the count of amplicons per fragment varies widely. However, the expected number of distinct fragment lengths is a function of the abundance of cells hosting an integration site at a given position and a certain nuisance parameter. The algorithm implicitly estimates that function to estimate the relative abundance.

This package performs estimation and testing of the treatment effect in a 2-group randomized clinical trial with a quantitative, dichotomous, or right-censored time-to-event endpoint. The method improves efficiency by leveraging baseline predictors of the endpoint. The inverse probability weighting technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used to provide unbiased estimation when the endpoint is missing at random.

Statistical tools for analyzing time-to-event data using machine learning. Implements survival stacking for conditional survival estimation, standardized survival function estimation for current status data, and methods for algorithm-agnostic variable importance. See Wolock CJ, Gilbert PB, Simon N, and Carone M (2024) <doi:10.1080/10618600.2024.2304070>.

This package provides functions to perform stepwise split regularized regression. The approach first uses a stepwise algorithm to split the variables into the models with a goodness of fit criterion, and then regularization is applied to each model. The weights of the models in the ensemble are determined based on a criterion selected by the user.

This package provides some code to run simulations of state-space models, and then use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) algorithm of Toni et al. (2009) <doi:10.1098/rsif.2008.0172> and a bootstrap particle filter based particle Markov chain Monte Carlo (PMCMC) algorithm (Andrieu et al., 2010 <doi:10.1111/j.1467-9868.2009.00736.x>). Also provides functions to plot and summarise the outputs.

Compute centrographic statistics (central points, standard distance, standard deviation ellipse, standard deviation box) for observations taken at point locations in 2D or 3D. The sfcentral library was inspired in aspace package but conceived to be used in a spatial tidyverse context.

This package implements two iterative techniques called T3Clus and 3Fkmeans, aimed at simultaneously clustering objects and a factorial dimensionality reduction of variables and occasions on three-mode datasets developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>. Also, we provide a convex combination of these two simultaneous procedures called CT3Clus and based on a hyperparameter alpha (alpha in [0,1], with 3FKMeans for alpha=0 and T3Clus for alpha= 1) also developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>. Furthermore, we implemented the traditional tandem procedures of T3Clus (TWCFTA) and 3FKMeans (TWFCTA) for sequential clustering-factorial decomposition (TWCFTA), and vice-versa (TWFCTA) proposed by P. Arabie and L. Hubert (1996) <doi:10.1007/978-3-642-79999-0_1>.