News for Package mathr

Version 0.1.3 (2026-08-31)

• Every help page now carries a runnable example. Where a base R equivalent exists the example shows both, so the agreement can be seen at a glance.

• Rbeta was wrong for every case except Beta(1, 1). It built the two gamma deviates with the shape and the scale arguments the other way round, so Rbeta(n, 2, 5) had mean 0.65 where the distribution has 2/7 = 0.286.

• Rgamma divided by bet in its alph == 1 branch where the other two branches multiply, so that one shape came out with mean 1/bet instead of bet.

• Rgamma0 never scaled the accepted deviate by alph, so every shape returned the same Exp(1) times bet: mean 1 rather than 3 for Rgamma0(n, 3, 1). It also now requires alph >= 1, which is the range its rejection step is valid over. Use Rgamma below that.

• simps13 had the Simpson weights 4 and 2 on the wrong points, which left it converging to the wrong value: 21.099 against a true 21.337 even at n = 60. It is now accurate to 1e-12 there.

• Both Simpson rules failed at their smallest legal n, 2 and 3, on a seq() with the wrong sign of step.

• The simps38 example asked for n = 10, which is not a multiple of 3, so the help page showed NULL.

• The deconv0 example divided by a spectrum containing a zero and printed Inf and NaN.

• The erfccheb title no longer claims a Chebyshev approximation, which it stopped using in 0.1.0.

Version 0.1.2 (2026-08-31)

• ChkFx left show.error.message holding a list instead of restoring it. options() returns the previous settings as a list, and that list was being passed back as the value. The restore now passes the list whole and runs from on.exit, so early returns are covered too.

• mlr(Plot = TRUE) no longer opens a device of its own and no longer leaves par() changed. It draws on the current device and restores the previous settings on exit.

• Diagnostics in mlr and deconv go through message() rather than cat(), so they can be suppressed and do not write to stdout.

• The ellipse examples no longer call dev.new().

• Packaging for CRAN: Authors@R replaces the free text author fields, the obsolete LazyLoad field is gone, and the reference manual is no longer shipped under inst/doc, since CRAN builds its own and the copy was 86 percent of the tarball.

Version 0.1.1 (2026-08-31)

• Qpois and Qbinom now follow the quantile convention of base R, the smallest k whose cumulative probability reaches p. They previously returned the largest k whose cumulative probability did not exceed p, one below stats::qpois and stats::qbinom for every p. The two now agree exactly over a wide parameter grid; see tests/.

• Qbinom(p, n, pe) returns n for p = 1, and both functions compare against a tolerance sized to the accuracy of Ppois and Pbinom, about 1e-12. Feeding a cumulative probability straight back in as p can still land on the other side of a tie, because those two are built from the incomplete gamma and beta rather than the saddle point algorithms base R uses.

Version 0.1.0 (2026-08-31)

• First release.

• Calculus, distribution functions, random variate generation, linear algebra, differential equations and optimization, sized for one semester.

• The special functions, and every distribution function built on them, follow freely licensed reference implementations, each attributed in the help page of the function concerned. gammln uses the Lanczos g=7 coefficients of the GNU Scientific Library. gser, gcf, gammp and gammq follow the Cephes series and continued fraction as redistributed in ALGLIB. betacf and betai follow the continued fraction and symmetry transform of the GNU Scientific Library. inverfc uses the Cephes normal quantile ndtri.

• erf and erfc come from the incomplete gamma function through erf(x) = P(1/2, x^2) and erfc(x) = Q(1/2, x^2). That needs no coefficient table of its own and stays accurate far into the tail: erfc holds to 1e-13 relative down to erfc(26) = 5.7e-296, where a rational fit in x alone has long since lost its significant digits.

• invgammp and invbetai are bracketed Newton iterations on the forward functions, following the strategy of gsl_cdf_gamma_Pinv and gsl_cdf_beta_Pinv. Above the median the residual is formed in the upper tail, which avoids the loss of about eight digits that a lower-tail residual suffers for p near 1.

• gammpapprox, betaiapprox and erfccheb are thin wrappers over gammp, betai and erfc.

• Every distribution function is checked against the base R equivalent over a wide parameter grid. See tests/.