Simulating bivariate survival data from copula models. Estimation of the association parameter in copula models. Two different ways to estimate the association parameter in copula models are implemented. A goodness-of-fit test for a given copula model is implemented. See Emura, Lin and Wang (2010) <doi:10.1016/j.csda.2010.03.013> for details.

This package provides an alternative approach to multiple testing by calculating a simultaneous upper confidence bounds for the number of true null hypotheses among any subset of the hypotheses of interest, using the methods of Goeman and Solari (2011) <doi:10.1214/11-STS356>.

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.

Cochran-Mantel-Haenszel methods (Cochran (1954) <doi:10.2307/3001616>; Mantel and Haenszel (1959) <doi:10.1093/jnci/22.4.719>; Landis et al. (1978) <doi:10.2307/1402373>) are a suite of tests applicable to categorical data. A competitor to those tests is the procedure of Nonparametric ANOVA which was initially introduced in Rayner and Best (2013) <doi:10.1111/anzs.12041>. The methodology was then extended in Rayner et al. (2015) <doi:10.1111/anzs.12113>. This package employs functions related to both methodologies and serves as an accompaniment to the book: An Introduction to Cochranâ Mantelâ Haenszel and Non-Parametric ANOVA. The package also contains the data sets used in that text.

Easily cache and retrieve computation results. The package works seamlessly across interactive R sessions, R scripts and Rmarkdown documents.

Implementation of conceptual properties norming studies, including estimates of CPNs parameters with their corresponding variances and estimates for the sampling process, and a sampling property function based on a modified empirical distribution from the original data.

Calculates the probabilities of k successes given n trials of a binomial random variable with non-negative correlation across trials. The function takes as inputs the scalar values the level of correlation or association between trials, the success probability, the number of trials, an optional input specifying the number of bits of precision used in the calculation, and an optional input specifying whether the calculation approach to be used is from Witt (2014) <doi:10.1080/03610926.2012.725148> or from Kuk (2004) <doi:10.1046/j.1467-9876.2003.05369.x>. The output is a (trials+1)-dimensional vector containing the likelihoods of 0, 1, ..., trials successes.

Measures morphological diversity from discrete character data and estimates evolutionary tempo on phylogenetic trees. Imports morphological data from #NEXUS (Maddison et al. (1997) <doi:10.1093/sysbio/46.4.590>) format with read_nexus_matrix(), and writes to both #NEXUS and TNT format (Goloboff et al. (2008) <doi:10.1111/j.1096-0031.2008.00217.x>). Main functions are test_rates(), which implements AIC and likelihood ratio tests for discrete character rates introduced across Lloyd et al. (2012) <doi:10.1111/j.1558-5646.2011.01460.x>, Brusatte et al. (2014) <doi:10.1016/j.cub.2014.08.034>, Close et al. (2015) <doi:10.1016/j.cub.2015.06.047>, and Lloyd (2016) <doi:10.1111/bij.12746>, and calculate_morphological_distances(), which implements multiple discrete character distance metrics from Gower (1971) <doi:10.2307/2528823>, Wills (1998) <doi:10.1006/bijl.1998.0255>, Lloyd (2016) <doi:10.1111/bij.12746>, and Hopkins and St John (2018) <doi:10.1098/rspb.2018.1784>. This also includes the GED correction from Lehmann et al. (2019) <doi:10.1111/pala.12430>. Multiple functions implement morphospace plots: plot_chronophylomorphospace() implements Sakamoto and Ruta (2012) <doi:10.1371/journal.pone.0039752>, plot_morphospace() implements Wills et al. (1994) <doi:10.1017/S009483730001263X>, plot_changes_on_tree() implements Wang and Lloyd (2016) <doi:10.1098/rspb.2016.0214>, and plot_morphospace_stack() implements Foote (1993) <doi:10.1017/S0094837300015864>. Other functions include safe_taxonomic_reduction(), which implements Wilkinson (1995) <doi:10.1093/sysbio/44.4.501>, map_dollo_changes() implements the Dollo stochastic character mapping of Tarver et al. (2018) <doi:10.1093/gbe/evy096>, and estimate_ancestral_states() implements the ancestral state options of Lloyd (2018) <doi:10.1111/pala.12380>. calculate_tree_length() and reconstruct_ancestral_states() implements the generalised algorithms from Swofford and Maddison (1992; no doi).

Computes a novel metric of affinity between two entities based on their co-occurrence (using binary presence/absence data). The metric and its MLE, alpha hat, were advanced in Mainali, Slud, et al, 2021 <doi:10.1126/sciadv.abj9204>. Various types of confidence intervals and median interval were developed in Mainali and Slud, 2022 <doi:10.1101/2022.11.01.514801>. The `finches` dataset is now bundled internally (no longer pulled via the cooccur package, which has been dropped).

Color palettes for EPL, MLB, NBA, NHL, and NFL teams.

Flexible tools to fit, tune and obtain absolute risk predictions from regularized cause-specific cox models with elastic-net penalty.

Cobb's maximum likelihood method for cusp-catastrophe modeling (Grasman, van der Maas, and Wagenmakers (2009) <doi:10.18637/jss.v032.i08>; Cobb (1981), Behavioral Science, 26(1), 75-78). Includes a cusp() function for model fitting, and several utility functions for plotting, and for comparing the model to linear regression and logistic curve models.

Composite likelihood approach is implemented to estimating statistical models for spatial ordinal and proportional data based on Feng et al. (2014) <doi:10.1002/env.2306>. Parameter estimates are identified by maximizing composite log-likelihood functions using the limited memory BFGS optimization algorithm with bounding constraints, while standard errors are obtained by estimating the Godambe information matrix.

The Citation File Format version 1.2.0 <doi:10.5281/zenodo.5171937> is a human and machine readable file format which provides citation metadata for software. This package provides core utilities to generate and validate this metadata.

This package implements lasso and ridge regression for dichotomised outcomes (<doi:10.1080/02664763.2023.2233057>), i.e., numerical outcomes that were transformed to binary outcomes. Such artificial binary outcomes indicate whether an underlying measurement is greater than a threshold.

This package provides a collection of coding functions as alternatives to the standard functions in the stats package, which have names starting with contr.'. Their main advantage is that they provide a consistent method for defining marginal effects in factorial models. In a simple one-way ANOVA model the intercept term is always the simple average of the class means.

Build graph/network structures using functions for stepwise addition and deletion of nodes and edges. Work with data available in tables for bulk addition of nodes, edges, and associated metadata. Use graph selections and traversals to apply changes to specific nodes or edges. A wide selection of graph algorithms allow for the analysis of graphs. Visualize the graphs and take advantage of any aesthetic properties assigned to nodes and edges.

It generates summary statistics on the input dataset using different descriptive univariate statistical measures on entire data or at a group level. Though there are other packages which does similar job but each of these are deficient in one form or other, in the measures generated, in treating numeric, character and date variables alike, no functionality to view these measures on a group level or the way the output is represented. Given the foremost role of the descriptive statistics in any of the exploratory data analysis or solution development, there is a need for a more constructive, structured and refined version over these packages. This is the idea behind the package and it brings together all the required descriptive measures to give an initial understanding of the data quality, distribution in a faster,easier and elaborative way.The function brings an additional capability to be able to generate these statistical measures on the entire dataset or at a group level. It calculates measures of central tendency (mean, median), distribution (count, proportion), dispersion (min, max, quantile, standard deviation, variance) and shape (skewness, kurtosis). Addition to these measures, it provides information on the data type, count on no. of rows, unique entries and percentage of missing entries. More importantly the measures are generated based on the data types as required by them,rather than applying numerical measures on character and data variables and vice versa. Output as a dataframe object gives a very neat representation, which often is useful when working with a large number of columns. It can easily be exported as csv and analyzed further or presented as a summary report for the data.

An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.

Represents the content of a directory as an interactive collapsible tree. Offers the possibility to assign a text (e.g., a Readme.txt') to each folder (represented as a clickable node), so that when the user hovers the pointer over a node, the corresponding text is displayed as a tooltip.

Query for metrics from Datadog (<https://www.datadoghq.com/>) via its API.

Calculates distances from point locations to features. The usual approach for eg. resource selection function analyses is to generate a complete distance to features surface then sample it with your observed and random points. Since these raster based approaches can be pretty costly with large areas, and often lead to memory issues in R, the distanceto package opts to compute these distances using efficient, vector based approaches. As a helper, there's a decidedly low-res raster based approach for visually inspecting your region's distance surface. But the workhorse is distance_to.

Estimation and testing methods for dependently truncated data. Semi-parametric methods are based on Emura et al. (2011)<Stat Sinica 21:349-67>, Emura & Wang (2012)<doi:10.1016/j.jmva.2012.03.012>, and Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>. Parametric approaches are based on Emura & Konno (2012)<doi:10.1007/s00362-014-0626-2> and Emura & Pan (2017)<doi:10.1007/s00362-017-0947-z>. A regression approach is based on Emura & Wang (2016)<doi:10.1007/s10463-015-0526-9>. Quasi-independence tests are based on Emura & Wang (2010)<doi:10.1016/j.jmva.2009.07.006>. Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.

The goal of dlr is to provide a friendly wrapper around the common pattern of downloading a file if that file does not already exist locally.