Regression Models with Break-Points / Change-Points Estimation (with Possibly Random Effects)

Fitting regression models where, in addition to possible linear terms, one or more covariates have segmented (i.e., broken-line or piece-wise linear) or stepmented (i.e. piece-wise constant) effects. Multiple breakpoints for the same variable are allowed. The estimation method is discussed in Muggeo (2003, ) and illustrated in Muggeo (2008, < https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf>). An approach for hypothesis testing is presented in Muggeo (2016, ), and interval estimation for the breakpoint is discussed in Muggeo (2017, ). Segmented mixed models, i.e. random effects in the change point, are discussed in Muggeo (2014, ). Estimation of piecewise-constant relationships and changepoints (mean-shift models) is discussed in Fasola et al. (2018, ).

Tests in Linear Mixed Effects Models

Provides p-values in type I, II or III anova and summary tables for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method. A Kenward-Roger method is also available via the pbkrtest package. Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available.

Generalized Linear Mixed Models using Template Model Builder

Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.

Estimation of Entropy, Mutual Information and Related Quantities

Implements various estimators of entropy for discrete random variables, including the shrinkage estimator by Hausser and Strimmer (2009), the maximum likelihood and the Millow-Madow estimator, various Bayesian estimators, and the Chao-Shen estimator. It also offers an R interface to the NSB estimator. Furthermore, the package provides functions for estimating the Kullback-Leibler divergence, the chi-squared divergence, mutual information, and the chi-squared divergence of independence. It also computes the G statistic and the chi-squared statistic and corresponding p-values. Furthermore, there are functions for discretizing continuous random variables.

Set (Normal) Random Number Generator and Seed

Provides utilities to help set and record the setting of the seed and the uniform and normal generators used when a random experiment is run. The utilities can be used in other functions that do random experiments to simplify recording and/or setting all the necessary information for reproducibility. See the vignette and reference manual for examples.

R Interface to the 'UNU.RAN' Random Variate Generators

Interface to the 'UNU.RAN' library for Universal Non-Uniform RANdom variate generators. Thus it allows to build non-uniform random number generators from quite arbitrary distributions. In particular, it provides an algorithm for fast numerical inversion for distribution with given density function. In addition, the package contains densities, distribution functions and quantiles from a couple of distributions.

Various R Programming Tools

Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion', 'getDependencies', 'keywords', 'scat'), - calculate the logit and inverse logit transformations ('logit', 'inv.logit'), - test if a value is missing, empty or contains only NA and NULL values ('invalid'), - manipulate R's .Last function ('addLast'), - define macros ('defmacro'), - detect odd and even integers ('odd', 'even'), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'), - perform a binary search ('binsearch'), - sort strings containing both numeric and character components ('mixedsort'), - create a factor variable from the quantiles of a continuous variable ('quantcut'), - enumerate permutations and combinations ('combinations', 'permutation'), - calculate and convert between fold-change and log-ratio ('foldchange', 'logratio2foldchange', 'foldchange2logratio'), - calculate probabilities and generate random numbers from Dirichlet distributions ('rdirichlet', 'ddirichlet'), - apply a function over adjacent subsets of a vector ('running'), - modify the TCP_NODELAY ('de-Nagle') flag for socket objects, - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'), - generate significance stars from p-values ('stars.pval'), - convert characters to/from ASCII codes ('asc', 'chr'), - convert character vector to ASCII representation ('ASCIIfy'), - apply title capitalization rules to a character vector ('capwords').

Implementation of Random Variables

Implements random variables by means of S4 classes and methods.

Toolkit for Encryption, Signatures and Certificates Based on OpenSSL

Bindings to OpenSSL libssl and libcrypto, plus custom SSH key parsers. Supports RSA, DSA and EC curves P-256, P-384, P-521, and curve25519. Cryptographic signatures can either be created and verified manually or via x509 certificates. AES can be used in cbc, ctr or gcm mode for symmetric encryption; RSA for asymmetric (public key) encryption or EC for Diffie Hellman. High-level envelope functions combine RSA and AES for encrypting arbitrary sized data. Other utilities include key generators, hash functions (md5, sha1, sha256, etc), base64 encoder, a secure random number generator, and 'bignum' math methods for manually performing crypto calculations on large multibyte integers.

Fast Multivariate Normal and Student's t Methods

Provides computationally efficient tools related to the multivariate normal and Student's t distributions. The main functionalities are: simulating multivariate random vectors, evaluating multivariate normal or Student's t densities and Mahalanobis distances. These tools are very efficient thanks to the use of C++ code and of the OpenMP API.