| mvsd.r |
1 |
1.5.5 |
19 |
"Computation of mean, variance and SD" |
| vecomp.r |
1 |
1.5.5 |
20 |
Vectorized computation of the double integral |
| my1sr.r |
1 |
1.5.6 |
23 |
An example for graphics in R |
| frlJLET.r |
1 |
1.56 |
24 |
Frequency of English letters in a Jack London novel |
| Jack_London_Call_of_the_Wild_The_f1.char |
1 |
1.56 |
24 |
"Char by char ""Call of the Wild""" |
| birthdaysim.r |
1 |
1.6 |
30 |
Simulations for the birthday problem |
| sampP.r |
1 |
1.8.1 |
38 |
Illustration of the sample command |
| sudoku.r |
1 |
1.8.2 |
40 |
Random sudoku problem |
| webhits.r |
2 |
2.1.2 |
45 |
Cdf of website hits |
| comwebhits.dat |
2 |
2.1.2 |
45 |
Data for website hits |
| webhitsQ.r |
2 |
2.2.1 |
55 |
Quartiles of web hits cdf |
| tr.binom.r |
2 |
2.2.2 |
57 |
Confidence range for binomial distribution |
| longpiece.r |
2 |
2.3 |
60 |
Simulations for the broken stick |
| simCookie.r |
2 |
2.3 |
61 |
Simulations for raisin in the cookie |
| truck.turn.r |
2 |
2.4 |
65 |
Simulations for safe turn |
| gampois.r |
2 |
2.6.1 |
78 |
Simulation for approximation Poison by gamma cdf |
| pti.gamma.r |
2 |
2.6.3 |
80 |
Newton's algorithm for the gamma tight confidence range |
| pti.gamma.rome.r |
2 |
2.6.3 |
81 |
Modified pti.gamma for Rome example |
| varX2.r |
2 |
2.7 |
87 |
Simulations for var(X^2) |
| mile.r |
2 |
2.9.1 |
97 |
Simulations for distance to work (LLN) |
| LLNintegral.r |
2 |
2.9.2 |
102 |
Integral approximation via Monte Carlo |
| LLNIntegral2.r |
2 |
2.9.2 |
103 |
Optimal lambda for Monte Carlo approximation |
| LLNintegral3.r |
2 |
2.1 |
106 |
Normal approximation of the binomial cdf |
| clt.binom.r |
2 |
2.1 |
106 |
Dynamic convergence of the binomial distribution |
| jb.r |
2 |
2.1 |
109 |
James Bond chase problem |
| cltP.r |
2 |
2.1 |
111 |
Violated CLT |
| lognDS.r |
2 |
2.11 |
117 |
Simulations for mean and variance of the lognormal distribution |
| logLnr.r |
2 |
2.11.1 |
119 |
Tight confidence range for the lognormal distribution |
| expr.r |
2 |
2.13 |
127 |
Simulations for logistic distribution |
| rugf2.r |
2 |
2.13 |
127 |
Newton's algorithm for Gaussian mixture |
| genmixN.r |
2 |
2.13 |
128 |
Random number generation for Gaussian mixture |
| discr.gen.r |
2 |
2.13 |
129 |
Random number generation from a discrete distribution |
| meanC.r |
2 |
2.13.1 |
130 |
Simulations for Cauchy distribution |
| pidistr.r |
2 |
2.15 |
136 |
Pmf of pi digits |
| pidistr1010.r |
2 |
2.15 |
137 |
The 10x10 probability matrix for pi digits |
| benfordEXP.r |
2 |
2.16 |
142 |
Benford's law for Pareto distribution |
| sales.RData |
2 |
2.16 |
142 |
Sales transactions data (R object) |
| benfordFT.r |
2 |
2.16 |
142 |
Benford's law analysis of fraudulent transactions |
| benfordN.r |
2 |
2.16.1 |
143 |
Almost-Benford's law distributions |
| fracksim.r |
2 |
2.16 |
144 |
Benford's law for the t-distribution |
| sbmultD.r |
3 |
3.1 |
152 |
Density surface viewed at different angle |
| CondDensity.mkv |
3 |
3.3 |
169 |
Conditional density movie file |
| mixedDens.r |
3 |
3.3.2 |
180 |
Gaussian mixture distribution of heights |
| randS.r |
3 |
3.3.3 |
184 |
Random sums |
| cancgr.r |
3 |
3.3.4 |
185 |
Simulations for cancer growth |
| heightweight.r |
3 |
3.5 |
202 |
Scatterplot weight versus height |
| dn2.r |
3 |
3.5 |
204 |
Contours of the bivariate normal density |
| oilspill.r |
3 |
3.5 |
206 |
Simulations for oil spill in the ocean |
| dn3.r |
3 |
3.5.3 |
209 |
Generation of random numbers from the bivariate normal distribution |
| dint.r |
3 |
3.5.3 |
213 |
Double integral approximation using simulations |
| copRN.r |
3 |
3.5.4 |
216 |
Contours of the copula density |
| ell2.r |
3 |
3.5.4 |
216 |
Contours of the bivariate normal density |
| filelXY.r |
3 |
3.6 |
221 |
Simulations for Fieller density |
| metpr2.r |
3 |
3.7.1 |
225 |
Simulations for meeting problem 2 |
| buffon.r |
3 |
3.7.2 |
225 |
Buffon's needle problem |
| stickSQ.r |
3 |
3.7.2 |
226 |
Simulations for random segment example |
| sbE.r |
3 |
3.7.2 |
227 |
"Simulations for ""Who wins the game?"" example" |
| s47.r |
3 |
3.7.2 |
228 |
Simulations for random squares |
| prmb.r |
3 |
3.8.4 |
235 |
Probability bullet |
| cdfMED.r |
3 |
3.9 |
238 |
Cdf of the median |
| pmaxmin.r |
3 |
3.9 |
238 |
Distribution of the range |
| cov01.r |
3 |
3.9 |
239 |
Simulations for random segments |
| normQQ.r |
3 |
3.10.2 |
247 |
Testing multivariate normal distribution |
| intrD3.r |
3 |
3.10.3 |
250 |
Simulations for the root of the random cubic equation |
| multB.r |
3 |
3.10.4 |
252 |
Simulations for the multinomial distribution |
| mn3.r |
4 |
4.1.1 |
260 |
Genetation and viewing of 3D points |
| foold.csv |
4 |
4.1.2 |
263 |
Data for temperature in March |
| multCLT.r |
4 |
4.1.3 |
269 |
Bivariate CLT |
| chidf1.r |
4 |
4.2.1 |
277 |
The cdf of the chi-square cdf |
| salary.r |
5 |
5.1 |
294 |
CDF of salary in Connecticut and Vermont |
| mortgageROC.csv |
5 |
5.1.1 |
301 |
Data on 375 mortgage applicants |
| mortgageROC.r |
5 |
5.1.1 |
302 |
ROC curve for mortgage applicants |
| vomit.r |
5 |
5.1.1 |
304 |
Time to vomiting ROC curve |
| emesis.txt |
5 |
5.1.1 |
304 |
Data for the vomit code |
| survROC.r |
5 |
5.1.1 |
307 |
ROC curve for cancer patients |
| survcanc.csv |
5 |
5.1.1 |
307 |
Data for the survROC code |
| SCcancer.r |
5 |
5.1.2 |
306 |
Survival analysis for cancer patients |
| DeathYears.csv |
5 |
5.1.1 |
306 |
Survival data for the SCcancer code |
| usbflash.csv |
5 |
5.1 |
311 |
Data for Problem 29 |
| creditpr.r |
5 |
5.1 |
312 |
1996 credit card applicants' analysis |
| creditpr.csv |
5 |
5.1 |
312 |
Data for the creditpr code |
| webhits.hist.r |
5 |
5.2 |
313 |
Histogram plot for website hits |
| histN.r |
5 |
5.2 |
315 |
Histogram and density for normal distribution |
| SCcancerQQ.r |
5 |
5.3 |
318 |
Q-q plot for cancer patients |
| Qqmurder.r |
5 |
5.3 |
318 |
Q-q plot to test the uniformity of murders |
| wikmurdr.txt |
5 |
5.3 |
318 |
Murder rates in 51 states |
| qqband.r |
5 |
5.3.1 |
320 |
Q-q plot for uniform distribution |
| qqLOGband.r |
5 |
5.3.1 |
321 |
Q-q plot for lognormal distribution |
| mortgageQQ.r |
5 |
5.3.1 |
322 |
Q-q plot for mortgage data |
| qqnill.r |
5 |
5.3 |
323 |
Problem 5 |
| toears.txt |
5 |
5.3 |
323 |
Toenail arsenic data |
| Goldman.csv |
5 |
5.3 |
323 |
Anatomical data |
| salaryBAR.r |
5 |
5.4 |
324 |
Barplot for Vermont and Connecticut salaries |
| kernavN.r |
5 |
5.5 |
326 |
Gaussian kernel densities |
| kernM.r |
5 |
5.5 |
327 |
Gaussian kernel densities with two bw |
| n.density.my.r |
5 |
5.5 |
327 |
In-house Gaussian kernel density |
| eppendorf.r |
5 |
5.5 |
329 |
Rat brain oxygen distribution |
| eppendorf.txt |
5 |
5.5 |
329 |
Data for rat brain oxygen |
| toears.r |
5 |
5.5 |
330 |
Distribution of toenail arsenic in NH |
| toears.txt |
5 |
5.5 |
330 |
Data for toenail distribution |
| asviol.r |
5 |
5.5 |
330 |
Asymmetric violin for salary in VT and CT |
| kern.movie.r |
5 |
5.5.1 |
332 |
Density movie |
| alc3d.r |
5 |
5.5.2 |
333 |
3D alcohol consumption |
| alcoholUSA.csv |
5 |
5.5.2 |
333 |
Data for 3D plots |
| autocrash.csv |
5 |
5.5 |
335 |
Automobile accident data |
| bvn.density.my.r |
5 |
5.6 |
337 |
Bivariate normal density |
| bvex.r |
5 |
5.6 |
337 |
2D and 3D bivariate kernel densities |
| Eyx.r |
5 |
5.6 |
337 |
E(Y|X=x) |
| matimage.r |
5 |
5.6.1 |
339 |
Matrix image |
| R.smooth.r |
5 |
5.6.1 |
340 |
Smoothed images of R |
| R.pgm |
5 |
5.6.1 |
340 |
The pgm image data for R |
| salmark.r |
5 |
5.6.2 |
342 |
Scatterplot of Forbes data |
| Forbes2000.csv |
5 |
5.6.2 |
342 |
Forbes data |
| nhcancer.r |
5 |
5.6.3 |
342 |
NH lung cancer spatial distribution |
| NHtowns.csv |
5 |
5.6.3 |
342 |
NH town names |
| xyNHcancer.csv |
5 |
5.6.3 |
343 |
Coordinates of 10439 cancer cases |
| xyNHpopulation.csv |
5 |
5.6.3 |
343 |
Geographic location of random NH residents |
| Lena.pgm |
5 |
5.6 |
346 |
Lena image |
| IBM_daily.csv |
5 |
5.6 |
346 |
IBM stock prices |
| robpol.r |
6 |
6.2 |
353 |
Police and bank robber |
| gMMgamma.r |
6 |
6.2.1 |
356 |
Newton's algorithm for the MM estimation of gamma parameters |
| pois2est.r |
6 |
6.4.2 |
371 |
RMSE for two estimators of lambda |
| luR.r |
6 |
6.4.3 |
373 |
Simulations for lower and upper bounds of uniform distribution |
| arMSE.r |
6 |
6.6.1 |
389 |
Simulations for estimation of area of the circle |
| robias.r |
6 |
6.6.2 |
393 |
Simulations for the bias of c.c. |
| cimcorSP.r |
6 |
6.6.2 |
395 |
17x17 stock correlation heatmap |
| stocks.zip |
6 |
6.6.2 |
394 |
Data on 17 stocks (must be unzipped) |
| Rcolor.pdf |
6 |
6.6.2 |
396 |
Colors in R (by name) |
| olsim.r |
6 |
6.7.2 |
405 |
Simulations for simple regression |
| lm.trendSP.r |
6 |
6.7.3 |
408 |
Prediction of the Google stock price |
| truckR.r |
6 |
6.7.4 |
411 |
Coefficient of determination for truck driers |
| truckR.data.csv |
6 |
6.7.4 |
411 |
Data for truck drivers' problem |
| betaMM.apply.r |
6 |
6.8.2 |
430 |
Simulations for the MM estimator for alpha and beta |
| betaMM.r |
6 |
6.8 |
433 |
MM estimation of gamma distribution |
| gammaInf.r |
6 |
6.9.2 |
449 |
ML estimation of alpha and beta of the gamma distribution |
| bufprob.r |
6 |
6.10.1 |
456 |
Estimation of L/D in the Buffon problem |
| regrD.r |
6 |
6.10.1 |
464 |
Linear regression with random X |
| father.son.csv |
6 |
6.10.1 |
466 |
Galton data for father and son heights |
| piest.r |
6 |
6.10.1 |
471 |
Comparison of four estimators of pi |
| gotobed.r |
6 |
6.10.2 |
474 |
When students go to bed |
| autocrash.r |
6 |
6.10.2 |
475 |
Parzen density for autocrash circular data |
| autocrash.csv |
6 |
6.10.2 |
475 |
Autocrash circular data |
| cubMLE.r |
6 |
6.10.5 |
494 |
ML estimation of the quadratic model |
| mleUNE.r |
6 |
6.10.5 |
497 |
MLE for unemployment rate |
| cauchy.theta.r |
6 |
6.10.6 |
502 |
MLE for the Cauchy distribution |
| cauchy.google.r |
6 |
6.10.6 |
503 |
MLE for the Google stock price |
| mle.gamma.OPT.r |
6 |
6.10.6 |
506 |
Comparison of three algorithms for ML estimation |
| mle.gamma.CT.r |
6 |
6.10.6 |
506 |
Estimation of people in poverty in CT |
| bufprobSA.r |
6 |
6.10.6 |
508 |
Simulation-based ML for the Buffon problem |
| ranlRest.r |
6 |
6.10.6 |
508 |
Estimation of the radius of the disk for the random lines problem |
| heightweight.r |
6 |
6.1 |
509 |
Scatterplot height versus weight of Korean young people |
| HeightWeight.csv |
6 |
6.1 |
509 |
Height and weight data |
| rws.r |
6 |
6.1 |
510 |
Random walk on the lattice square |
| meanmed.r |
6 |
6.11 |
516 |
Simulations for mean and median in Laplace distribution |
| robloc.r |
6 |
6.11.1 |
519 |
Estimation for noisy data via Gaussian mixture |
| gng.r |
6 |
6.11.1 |
521 |
Estimation of Google stock price using Gaussian mixture |
| AMZN.csv |
6 |
6.11 |
522 |
Amazon stock prices |
| pvsim.r |
7 |
7.1.1 |
526 |
Illustration of p-value using simulations |
| boengtr.csv |
7 |
7.1.2 |
530 |
Data for Boeing inside trading |
| NHBirths2003_2009.csv |
7 |
7.2 |
533 |
Data on 33666 NH newborns |
| houseprice.txt |
7 |
7.2 |
534 |
House prices |
| familyincome.r |
7 |
7.2 |
535 |
Poverty test |
| pvalcost.r |
7 |
7.4 |
550 |
Simulations for the p-value living cost example |
| powsim.r |
7 |
7.4.2 |
553 |
Confirmation of the power of the t-test via simulations |
| stucost.r |
7 |
7.4.2 |
554 |
Living cost for freshmen and sophomores |
| stucost.csv |
7 |
7.4.2 |
554 |
Data for living cost |
| ttest2pow.r |
7 |
7.4.2 |
557 |
Simulations for Welsh and t-test |
| sampt2.r |
7 |
7.4.3 |
557 |
One- versus two-sided t-test |
| salaryMW.r |
7 |
7.4.4 |
559 |
Testing the salary for men and women |
| salaryMW_paired.csv |
7 |
7.4.4 |
559 |
Salary data |
| nonpN.r |
7 |
7.4.4 |
560 |
Comparison of power functions for parametric and nonparametric test |
| varq.r |
7 |
7.5.1 |
563 |
Two-sided variance test |
| vartest.r |
7 |
7.5.1 |
563 |
Equal-tail probabilities and unbiased test for variance |
| smallF.r |
7 |
7.6.2 |
572 |
Newton's algorithm for unbiased test for variances |
| vartestSP.r |
7 |
7.6.2 |
573 |
Testing the volatility of two stocks |
| binprop1.r |
7 |
7.6.3 |
575 |
Simulations for the power function of the binomial test |
| binprop2.r |
7 |
7.6.3 |
577 |
Simulations for the test of two binomial proportions |
| poistest.r |
7 |
7.6.4 |
578 |
Power function for the Poison distribution |
| poissamn.r |
7 |
7.6.4 |
579 |
Sample size for the Poisson distribution |
| vartestSP2.r |
7 |
7.6 |
580 |
Variance test for GOOGLE and AMAZON |
| GOOG.csv |
7 |
7.6 |
580 |
Data for GOOGLE |
| AMZN.csv |
7 |
7.6 |
580 |
Data for AMZN |
| corn0.r |
7 |
7.7 |
581 |
Power functions for correlation coefficient |
| cor0.r |
7 |
7.7 |
582 |
Simulations for testing correlation coefficient |
| ciumax.r |
7 |
7.8 |
585 |
CI for uniform distribution |
| Cimovie.r |
7 |
7.8 |
586 |
CI animation |
| houprCI.r |
7 |
7.8 |
588 |
CI and confidence range for the house price |
| civar.r |
7 |
7.8.3 |
592 |
Shortest CI for variance and SD |
| ci.binpr.r |
7 |
7.8.4 |
593 |
Shortest CI for the binomial probability |
| cfmus.r |
7 |
7.8.5 |
596 |
"Simulations for confidence region for (mu,sigma)" |
| thastest.r |
7 |
7.9 |
600 |
Three asymptotic tests for binomial probability |
| powlinmod.r |
7 |
7.9 |
602 |
Simulations for testing the regression coefficient by three tests |
| thlinmod.r |
7 |
7.9 |
602 |
Simulation-derived cdfs for four tests |
| powlinmodC.r |
7 |
7.9 |
604 |
Type I adjustments for three tests |
| chismult.r |
7 |
7.9.1 |
607 |
Simulations for chi-square and Wald tests |
| frlJL.r |
7 |
7.9.1 |
607 |
Wald test for English letter analysis |
| Mark_Twain_The_Adventures_of_Tom_Sawyer_f1.txt.char |
7 |
7.9.1 |
607 |
Mark Twain novel char data |
| mnist_train.csv.zip |
7 |
7.9.2 |
608 |
Handwritten digit train set |
| mnist_test.csv |
7 |
7.9.2 |
608 |
Handwritten digit test set |
| dig.mnist.r |
7 |
7.9.2 |
609 |
Plotting and classification of handwritten digits |
| wtext.r |
7 |
7.9 |
611 |
Analysis of English letters in the novel by Jane Austen |
| Jane_Austen_Pride_and_Prejudice.char |
7 |
7.9 |
611 |
"Char by char ""Pride and Prejudice""" |
| saldisc.r |
7 |
7.1 |
623 |
Drug or not to drug example |
| saldisc.csv |
7 |
7.1 |
623 |
Data for the saldisc code |
| dvalREG.r |
7 |
7.1 |
626 |
d-value for linear regression |
| simLM.r |
8 |
8.3.1 |
647 |
Simulations for statistical properties of the quadratic regression |
| roblinreg2.r |
8 |
8.3.1 |
648 |
Simulations to study violation of the normal assumption |
| roblinreg.r |
8 |
8.3.1 |
649 |
Simulations with the apply function |
| CDpf.r |
8 |
8.4.1 |
653 |
Estimation of the Cobb-Douglas production function |
| CDpf.csv |
8 |
8.4.1 |
653 |
Data for pf |
| qtrpow.r |
8 |
8.4.3 |
660 |
Sample size determination for the regression coefficient |
| linpower.r |
8 |
8.4.3 |
660 |
Simulations for the F-test in quadratic regression |
| simCB.r |
8 |
8.4.3 |
664 |
Simultaneous confidence band for quadratic regression |
| olsnormT.r |
8 |
8.4.5 |
668 |
Simulations for linear regression with random predictor |
| pfx123.csv |
8 |
8.4 |
670 |
Data for pf from Problem 8 |
| dvalPMED.r |
8 |
8.5.2 |
675 |
D-value for personalized medicine |
| dvalPMED.csv |
8 |
8.5.2 |
675 |
Data for dvalPMED |
| kidsdrink.r |
8 |
8.6.1 |
677 |
Kids drinking alcohol example |
| kidsdrink.csv |
8 |
8.6.1 |
677 |
Data for kids drinking |
| leftright.r |
8 |
8.6.2 |
680 |
False discovery example |
| leftright.csv |
8 |
8.6.2 |
680 |
Data for false discovery example |
| hfn.r |
8 |
8.6.3 |
682 |
"Heigh, foot, and nose example" |
| HeightFootNose.csv |
8 |
8.6.3 |
682 |
"Data for height, foot, and nose example" |
| amzn.r |
8 |
8.6.5 |
692 |
Autoregression for AMZN stock price |
| AMZN_weekly.csv |
8 |
8.6.5 |
691 |
Data for AMZN autoregression example |
| salMW.r |
8 |
8.7.1 |
697 |
Gender difference in salary |
| Salary.csv |
8 |
8.7.1 |
696 |
Salary data for men and women |
| nile.r |
8 |
8.7.1 |
700 |
Nile river example |
| NileFlow.csv |
8 |
8.7.1 |
700 |
Data for Nile flow |
| housepr.r |
8 |
8.7.1 |
701 |
Regressions for house price in two areas |
| houseprice.csv |
8 |
8.7.1 |
701 |
Data for house prices in two areas |
| obesegene.r |
8 |
8.7.1 |
702 |
BMI-gene interaction example |
| obesegene.csv |
8 |
8.7.1 |
702 |
Data for the BMI-gene regression |
| simpson.r |
8 |
8.7.1 |
704 |
Simpson paradox |
| simpson.csv |
8 |
8.7.2 |
704 |
Data for Simpson paradox example |
| movrat.r |
8 |
8.7.2 |
706 |
Movie rating example |
| movrat.csv |
8 |
8.7.2 |
706 |
Data for movie rating |
| BPlong.r |
8 |
8.7.3 |
708 |
Blood pressure treatment |
| BPdata.csv |
8 |
8.7.3 |
708 |
Data for blood pressure |
| QoL.csv |
8 |
8.7.3 |
710 |
Quality of life data |
| qolS.r |
8 |
8.7.3 |
710 |
Quality of life example |
| flu.r |
8 |
8.7.4 |
714 |
Flu incidence ANOVA example |
| linrep.r |
8 |
8.7.4 |
716 |
Regression on averages and ANOVA |
| consIR.r |
8 |
8.7.5 |
721 |
Internet radio example |
| consIR.csv |
8 |
8.7.5 |
721 |
Data for internet radio example |
| CollegeSalary.csv |
8 |
8.7 |
772 |
Salary data on 36 college employees |
| walD.r |
8 |
8.8 |
724 |
Black Friday shopping example |
| blackfriday.csv |
8 |
8.8 |
724 |
Data for Black Friday shoppers |
| lungsm.r |
8 |
8.8.2 |
733 |
Two-by-two table via logistic regression |
| geodrink.r |
8 |
8.8.2 |
735 |
Binge drinking among kids |
| kidsdrinkDAT.csv |
8 |
8.8.2 |
735 |
Data for binge drinking |
| poisR.r |
8 |
8.8.3 |
737 |
Poisson regression for traffic tickets |
| Traffic.Viol.csv |
8 |
8.8.3 |
737 |
Data for traffic violations |
| cloglogV.r |
8 |
8.8.3 |
738 |
Poisson and log-log regression |
| amazshop.csv |
8 |
8.8 |
740 |
Data for Problem 10 |
| marathon.r |
9 |
9.5 |
770 |
Marathon nonlinear regression example |
| marathonWR2.txt |
9 |
9.5 |
770 |
Data for marathon records |
| dnaRAD.csv |
9 |
9.5 |
773 |
Cell survival data |
| dnaRAD.r |
9 |
9.5 |
774 |
Change-point nonlinear regression for cell survival |
| twocph.r |
9 |
9.5 |
775 |
Two-compartment pharmacokinetics model |
| twocph.csv |
9 |
9.5 |
775 |
Data for the two-compartment example |
| twocphCI.r |
9 |
9.5 |
778 |
Three CIs for the two-compartment model |
| ces.r |
9 |
9.5 |
780 |
CES and Cobb-Douglas production functions |
| CES.csv |
9 |
9.5 |
780 |
Data for the ces R code |
| FallingHat.csv |
9 |
9.5 |
781 |
Data for the falling example |
| freefall.r |
9 |
9.5 |
781 |
The R code for the falling hat example |
| gammaNLS.r |
9 |
9.5 |
784 |
Simulations for the gamma distribution |
| SCcancerQQ2.r |
9 |
9.5 |
784 |
Mixture exponential distribution |
| nen.r |
9 |
9.6.1 |
787 |
Comparison of three distribution approximations |
| power.nls.r |
9 |
9.6.2 |
789 |
Comparison of three power functions |
| expgr.r |
9 |
9.6.3 |
791 |
Confidence region for the two-parameter regression |
| ci.nls.r |
9 |
9.6.4 |
792 |
Three CIs |
| q2pr.r |
9 |
9.7.2 |
798 |
Probability of two local minima |
| michm.r |
9 |
9.9.1 |
806 |
Michaelis-Menten nonlinear regression |
| berndet.r |
10 |
10.2 |
818 |
Bernoulli matrix |
| block.inv.r |
10 |
10.2 |
818 |
Block inverse |
| nLINEXP.r |
10 |
10.6.3 |
836 |
Newton's algorithm for a nonlinear equation |
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