Tests the goodness-of-fit to the Normal distribution for the errors of an ARMA model.

This package provides functions to compute the non-negative garrote estimator as proposed by Breiman (1995) <https://www.jstor.org/stable/1269730> with the penalized initial estimators extension as proposed by Yuan and Lin (2007) <https://www.jstor.org/stable/4623260>.

Estimation of structural equation models with nonlinear effects and underlying nonnormal distributions.

To add the table of numbers at risk below the Kaplan-Meier plot.

This package provides a set of functions to visualize National Football League analysis in ggplot2 plots and gt tables.

Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited. Seven normality test are implemented. The asymptotic Lobato & Velasco's, asymptotic Epps, Psaradakis and Vávra, Lobato & Velasco's and Epps sieve bootstrap approximations, El bouch et al., and the random projections tests for univariate stationary process. Some other diagnostics such as, unit root test for stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. Additionally, the El bouch test performs normality tests for bivariate time series. The package also offers residual diagnostic for linear time series models developed in several packages.

This package provides a function designed to estimate the minimal sample size required to attain a specific statistical power in the context of linear regression and logistic regression models through simulations.

This package provides functions to access NASA's Earth Imagery and Assets API and the Earth Observatory Natural Event Tracker (EONET) webservice.

This package provides tools for working with the National Hydrography Dataset, with functions for querying, downloading, and networking both the NHD <https://www.usgs.gov/national-hydrography> and NHDPlus <https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plus> datasets.

This package provides a tool set for food information and dietary assessment. It uses food composition data from several reference databases, including: USDA (United States), CIQUAL (France), BEDCA (Spain), CNF (Canada) and STFCJ (Japan). NutrienTrackeR calculates the intake levels for both macronutrient and micronutrients, and compares them with the recommended dietary allowances (RDA). It includes a number of visualization tools, such as time series plots of nutrient intake, and pie-charts showing the main foods contributing to the intake level of a given nutrient. A shiny app exposing the main functionalities of the package is also provided.

The aim is to develop an R package, which is the new.dist package, for the probability (density) function, the distribution function, the quantile function and the associated random number generation function for discrete and continuous distributions, which have recently been proposed in the literature. This package implements the following distributions: The Power Muth Distribution, a Bimodal Weibull Distribution, the Discrete Lindley Distribution, The Gamma-Lomax Distribution, Weighted Geometric Distribution, a Power Log-Dagum Distribution, Kumaraswamy Distribution, Lindley Distribution, the Unit-Inverse Gaussian Distribution, EP Distribution, Akash Distribution, Ishita Distribution, Maxwell Distribution, the Standard Omega Distribution, Slashed Generalized Rayleigh Distribution, Two-Parameter Rayleigh Distribution, Muth Distribution, Uniform-Geometric Distribution, Discrete Weibull Distribution.

We connect the multi-class Neyman-Pearson classification (NP) problem to the cost-sensitive learning (CS) problem, and propose two algorithms (NPMC-CX and NPMC-ER) to solve the multi-class NP problem through cost-sensitive learning tools. Under certain conditions, the two algorithms are shown to satisfy multi-class NP properties. More details are available in the paper "Neyman-Pearson Multi-class Classification via Cost-sensitive Learning" (Ye Tian and Yang Feng, 2021).

Macros to generate nimble code from a concise syntax. Included are macros for generating linear modeling code using a formula-based syntax and for building for() loops. For more details review the nimble manual: <https://r-nimble.org/html_manual/cha-writing-models.html#subsec:macros>.

In empirical studies, instrumental variable (IV) regression is the signature method to solve the endogeneity problem. If we enforce the exogeneity condition of the IV, it is likely that we end up with a large set of IVs without knowing which ones are good. Also, one could face the model uncertainty for structural equation, as large micro dataset is commonly available nowadays. This package uses adaptive group lasso and B-spline methods to select the nonparametric components of the IV function, with the linear function being a special case (naivereg). The package also incorporates two stage least squares estimator (2SLS), generalized method of moment (GMM), generalized empirical likelihood (GEL) methods post instrument selection, logistic-regression instrumental variables estimator (LIVE, for dummy endogenous variable problem), double-selection plus instrumental variable estimator (DS-IV) and double selection plus logistic regression instrumental variable estimator (DS-LIVE), where the double selection methods are useful for high-dimensional structural equation models. The naivereg is nonparametric version of ivregress in Stata with IV selection and high dimensional features. The package is based on the paper by Q. Fan and W. Zhong, "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective" (2018), Journal of Business & Economic Statistics <doi:10.1080/07350015.2016.1180991> as well as a series of working papers led by the same authors.

This package provides a variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods. Its use is illustrated via a number of empirical applications and simulated examples.

Optimization for nonlinear objective and constraint functions. Linear or nonlinear equality and inequality constraints are allowed. It accepts the input parameters as a constrained matrix.

Includes functions and examples to compute NEAT, the Network Enrichment Analysis Test described in Signorelli et al. (2016, <DOI:10.1186/s12859-016-1203-6>).

This package implements the nonparametric causality-in-quantiles test (in mean or variance), returning a test object with an S3 plot() method. The current implementation uses one lag of each series (first-order Granger causality setup). Methodology is based on Balcilar, Gupta, and Pierdzioch (2016a) <doi:10.1016/j.resourpol.2016.04.004> and Balcilar et al. (2016) <doi:10.1007/s11079-016-9388-x>.

This package provides a finite-population significance test of the sharp causal null hypothesis that treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers. A generalized likelihood ratio test statistic is used, and the resulting p-value is exact. Currently, it is assumed that there are only Compliers and Never Takers in the population.

Includes five particle filtering algorithms for use with state space models in the nimble system: Auxiliary', Bootstrap', Ensemble Kalman filter', Iterated Filtering 2', and Liu-West', as described in Michaud et al. (2021), <doi:10.18637/jss.v100.i03>. A full User Manual is available at <https://r-nimble.org>.

This package provides a tool for drawing sassy UML (Unified Modeling Language) diagrams based on a simple syntax, see <https://www.nomnoml.com>. Supports styling, R Markdown and exporting diagrams in the PNG format. Note: you need a chromium based browser installed on your system.

The robustness of many of the statistical techniques, such as factor analysis, applied in the social sciences rests upon the assumption of item-level normality. However, when dealing with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964) <http://www.jstor.org/stable/2984418> provides an optimal transformation for non-normal variables. Yet, for large datasets of continuous variables, its application in current software programs is cumbersome with analysts having to take several steps to normalise each variable. We present an R package normalr that enables researchers to make convenient optimal transformations of multiple variables in datasets. This R package enables users to quickly and accurately: (1) anchor all of their variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated, (3) apply each unique exponent to its variable, (4) rescale resultant values to within their original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness, kurtosis, and other inferential assessments of normality.

Utilities for Natural Language Processing.

In the working paper titled "Why You Should Never Use the Hodrick-Prescott Filter", James D. Hamilton proposes a new alternative to economic time series filtering. The neverhpfilter package provides functions and data for reproducing his work. Hamilton (2017) <doi:10.3386/w23429>.