The COSSO regularization method automatically estimates and selects important function components by a soft-thresholding penalty in the context of smoothing spline ANOVA models. Implemented models include mean regression, quantile regression, logistic regression and the Cox regression models.

This package provides functions for completing and recalculating rankings and sorting.

In metabolic flux experiments tracer molecules (often glucose containing labelled carbon) are incorporated in compounds measured using mass spectrometry. The mass isotopologue distributions of these compounds needs to be corrected for natural abundance of labelled carbon and other effects, which are specific on the compound and ionization technique applied. This package provides functions to correct such effects in gas chromatography atmospheric pressure chemical ionization mass spectrometry analyses.

This package provides a convenient interface for making requests directly to the Civis Platform API <https://www.civisanalytics.com/platform>. Full documentation available here <https://civisanalytics.github.io/civis-r/>.

This package provides some tabulated data to be be referred to in a discussion in a vignette accompanying my upcoming R package playWholeHandDriverPassParams'. In addition to that specific purpose, these may also provide data and illustrate some computational approaches that are relevant to card games like hearts or bridge.This package refers to authentic data from Gregory Stoll <https://gregstoll.com/~gregstoll/bridge/math.html>, and details of performing the probability calculations from Jeremy L. Martin <https://jlmartin.ku.edu/~jlmartin/bridge/basics.pdf>.

Encrypts and decrypts using basic ciphers. None of these should be used in place of real encryption using state of the art tools. The ciphers included use methods described in the ciphers's Wikipedia and cryptography hobby websites.

This package provides a suite of computer model test functions that can be used to test and evaluate algorithms for Bayesian (also known as sequential) optimization. Some of the functions have known functional forms, however, most are intended to serve as black-box functions where evaluation requires running computer code that reveals little about the functional forms of the objective and/or constraints. The primary goal of the package is to provide users (especially those who do not have access to real computer models) a source of reproducible and shareable examples that can be used for benchmarking algorithms. The package is a living repository, and so more functions will be added over time. For function suggestions, please do contact the author of the package.

This package provides functions to analyze coarse data. Specifically, it contains functions to (1) fit parametric accelerated failure time models to interval-censored survival time data, and (2) estimate the case-fatality ratio in scenarios with under-reporting. This package's development was motivated by applications to infectious disease: in particular, problems with estimating the incubation period and the case fatality ratio of a given disease. Sample data files are included in the package. See Reich et al. (2009) <doi:10.1002/sim.3659>, Reich et al. (2012) <doi:10.1111/j.1541-0420.2011.01709.x>, and Lessler et al. (2009) <doi:10.1016/S1473-3099(09)70069-6>.

Offers several functions for Configural Frequencies Analysis (CFA), which is a useful statistical tool for the analysis of multiway contingency tables. CFA was introduced by G. A. Lienert as Konfigurations Frequenz Analyse - KFA'. Lienert, G. A. (1971). Die Konfigurationsfrequenzanalyse: I. Ein neuer Weg zu Typen und Syndromen. Zeitschrift für Klinische Psychologie und Psychotherapie, 19(2), 99â 115.

This package provides functions to test and compare causal models using Confirmatory Path Analysis.

Includes functions for the analysis of circular data using distributions based on Nonnegative Trigonometric Sums (NNTS). The package includes functions for calculation of densities and distributions, for the estimation of parameters, for plotting and more.

Detection of change-points for variance of heteroscedastic Gaussian variables with piecewise constant variance function. Adelfio, G. (2012), Change-point detection for variance piecewise constant models, Communications in Statistics, Simulation and Computation, 41:4, 437-448, <doi:10.1080/03610918.2011.592248>.

It fits finite mixture models for censored or/and missing data using several multivariate distributions. Point estimation and asymptotic inference (via empirical information matrix) are offered as well as censored data generation. Pairwise scatter and contour plots can be generated. Possible multivariate distributions are the well-known normal, Student-t and skew-normal distributions. This package is an complement of Lachos, V. H., Moreno, E. J. L., Chen, K. & Cabral, C. R. B. (2017) <doi:10.1016/j.jmva.2017.05.005> for the multivariate skew-normal case.

Variable selection for Gaussian model-based clustering as implemented in the mclust package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting mclust models. By default the algorithm uses a sequential search, but parallelisation is also available.

ClickHouse (<https://clickhouse.com/>) is an open-source, high performance columnar OLAP (online analytical processing of queries) database management system for real-time analytics using SQL. This DBI backend relies on the ClickHouse HTTP interface and support HTTPS protocol.

Retrieves crypto currency information and historical prices as well as information on the exchanges they are listed on. Historical data contains daily open, high, low and close values for all crypto currencies. All data is scraped from <https://coinmarketcap.com> via their web-api'.

This code provides several different functions for cleaning and analyzing continuous glucose monitor data. Currently it works with Dexcom', iPro 2', Diasend', Libre', or Carelink data. The cleandata() function takes a directory of CGM data files and prepares them for analysis. cgmvariables() iterates through a directory of cleaned CGM data files and produces a single spreadsheet with data for each file in either rows or columns. The column format of this spreadsheet is compatible with REDCap data upload. cgmreport() also iterates through a directory of cleaned data, and produces PDFs of individual and aggregate AGP plots. Please visit <https://github.com/childhealthbiostatscore/R-Packages/> to download the new-user guide.

Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods; see "Contrast trees and distribution boosting", Jerome H. Friedman (2020) <doi:10.1073/pnas.1921562117>. In situations where inaccuracies are detected, boosted contrast trees can often improve performance. Functions are provided to to build such trees in addition to a special case, distribution boosting, an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.

An end-to-end framework that enables users to implement various descriptive studies for a given set of target and outcome cohorts for data mapped to the Observational Medical Outcomes Partnership Common Data Model.

This package provides a daily summary of the Coronavirus (COVID-19) cases by state/province. Data source: Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus <https://systems.jhu.edu/research/public-health/ncov/>.

Calculate the theoretical value of convertible bonds by given parameters, including B-S theory and Monte Carlo method.

Procedures include Phillips (1995) FMVAR <doi:10.2307/2171721>, Kitamura and Phillips (1997) FMGMM <doi:10.1016/S0304-4076(97)00004-3>, Park (1992) CCR <doi:10.2307/2951679>, and so on. Tests with 1 or 2 structural breaks include Gregory and Hansen (1996) <doi:10.1016/0304-4076(69)41685-7>, Zivot and Andrews (1992) <doi:10.2307/1391541>, and Kurozumi (2002) <doi:10.1016/S0304-4076(01)00106-3>.

This package provides tools to easily access and analyze Canadian Election Study data. The package simplifies the process of downloading, cleaning, and using CES datasets for political science research and analysis. The Canadian Election Study ('CES') has been conducted during federal elections since 1965, surveying Canadians on their political preferences, engagement, and demographics. Data is accessed from multiple sources including the Borealis Data repository <https://borealisdata.ca/> and the official Canadian Election Study website <https://ces-eec.arts.ubc.ca/>. This package is not officially affiliated with the Canadian Election Study, Borealis Data, or the University of British Columbia, and users should cite the original data sources in their work.

This package implements the adaptive designs for integrated phase I/II trials of drug combinations via continual reassessment method (CRM) to evaluate toxicity and efficacy simultaneously for each enrolled patient cohort based on Bayesian inference. It supports patients assignment guidance in a single trial using current enrolled data, as well as conducting extensive simulation studies to evaluate operating characteristics before the trial starts. It includes various link functions such as empiric, one-parameter logistic, two-parameter logistic, and hyperbolic tangent, as well as considering multiple prior distributions of the parameters like normal distribution, gamma distribution and exponential distribution to accommodate diverse clinical scenarios. Method using Bayesian framework with empiric link function is described in: Wages and Conaway (2014) <doi:10.1002/sim.6097>.